Ecolog-L ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07)
ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/ <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr06/> *31st - March - 4th April 2025* Please feel free to share! We encourage attendees to bring their own data, you will receive opportunities to discuss your data with the instructor throughout the course, if you would like guideline on how to organize your data prior to the course please ask oliverhooo...@prstatistica.com This course is suitable for researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches, with applications to biogeography, spatial ecology, biodiversity conservation and related disciplines. This 5-day course will cover R concepts, methods, and tools that can be used to analyze community ecology data. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity indices, distance measures and distance-based multivariate methods, clustering, classification and ordination techniques using the R package VEGAN. We will use real-world empirical data sets to motivate analyses, such as describing patterns along gradients of environ-mental or anthropogenic disturbances, and quantifying the effects of continuous and discrete predictors. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures, guided computer coding, and participant exercises. The course is intended for intermediate users of R who are interested in community ecology, particularly in the areas of terrestrial and wetland ecology, microbial ecology, and natural resource management. You are strongly encouraged to use your own data sets (they should be clean and already structured, see the document: “recommendation if you participate with your data”. *Classes will run from 08:00 – 13:00 for the morning lecture and 14:00 – 16:00 for the practical (UK time) with an evening time session tbc for US, Canada etc. attendees. The course will be recorded and made available each day and will remain available for 28 days after the course for you to revisit any lectures.* DAY 1 • Module 1: Introduction to community data analysis, basics of programming in R • Module 2: Diversity analysis, species-abundance distributions DAY 2 • Module 3: Distance and transformation measures • Module 4: Clustering and classification analysis DAY 3 • Module 5: Unconstrained ordinations: Principal Component Analysis • Module 6: Other unconstrained ordinations DAY 4 • Module 7: Constrained ordinations: RDA and other canonical analysis • Module 8: Statistical tests for multivariate data and variation partitioning DAY 5 • Module 9: Overview of Spatial analysis, and recent Hierarchical Modeling of Species Communities (HMSC) methods • Modules 10: Special topics and discussion, analyzing participants’ data. Please email oliverhoo...@prstatistics.com with any questions -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Genome Assembly and Annotation (GAAA01) - FINAL CALL
ONLINE COURSE – Genome Assembly and Annotation (GAAA01) We only have 4 places left on next week's course! https://www.prstats.org/course/genome-assembly-and-annotation-gaaa01/ 4th - 6th November 2024 Please feel free to share! COURSE OVERVIEW - Genome assembly is the process of piecing together fragments of DNA to reconstruct the original genome. The genome provides crucial information for understanding genetic structure, function and variation. In recent years, long-read sequencing technologies have revolutionized genome assembly. These long reads can span repetitive sequences and structural variations making genome assembly simpler but also reducing gaps and fragments in the genome, resolve repeats, help with the detection of structural variation as well as improved haplotype phasing. During this course we will look at data generated using PacBio and Oxford Nanopore, discuss the pros and cons of both sequencing technologies and the effect they might have on genome assembly. During the course we will look at different tools available to generate assemblies, focussing on de novo genome assembly. Polishing using short or long reads and the introduction of Hi-C sequencing can increase completeness of the genomes. At the difference steps during the assembly process we will look at the contiguity, completeness and correctness of the generated genomes, thereby evaluation the status of the genome. Once a genome has been assembled the next step is annotation. Genome annotation involves identifying and mapping locations of genes and other functional elements within the sequenced genome. We will take a look at the differences between prokaryote and eukaryote genomes and the tools available for annotation. We will talk about steps to improve annotation once the automatic annotation has been made. By the end of the course, participants should: Know the difference between Nanopore and PacBio data Be able to assembly genomes Be able to assess the generated genomes Assemble genomes integrating Hi-C data Know how to annotate a genome Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L FINAL CALL - ONLINE COURSE – Introduction to Single Cell Analysis (ISCA01)
FINAL CALL - ONLINE COURSE – Introduction to Single Cell Analysis (ISCA01) https://www.prstats.org/course/introduction-to-single-cell-analysis-isca01/ 2nd - 4th December 2024 Please feel free to share! COURSE OVERVIEW - Take your RNA-Seq analysis to the next level with single cell RNA-Seq. This technology allows insights with an unpredicted level of detail, but that brings a new level of complexity to the data analysis. In this course, we will learn about the most popular single cell platforms, how to plan a scRNA-Seq experiment, deal with some of the many pitfalls when analysing your data, and effectively gain exciting, and cell type specific biological insights By the end of the course participants should: - Understand the basic principles of popular single cell platforms and the pros and cons of the different technologies. - Be able run standard software to process raw 10x Genomics and Parse Bioscience data and interpret the outputs - Understand how to use the ‘Trailmaker’ to quickly analyse scRNA-Seq data. - Understand the basics of the R Bioconductor ‘Seurat’ package, and how to combine it with other tools. - Understand how to perform appropriate data quality control and filtering. - Understand how to cluster cells both within and between samples, and identify possible cell types of individual cells and clusters - Understand how to use statistically robust methods to compare gene expression between samples to identify cell type specific changes in gene expression and potential pathways of interest. Please email oliverhoo...@prstatistics.com with any questions. Upcoming courses ONLINE COURSE – Introduction to Machine Learning using R and Rstudio (IMLR02) This course will be delivered live <https://www.prstats.org/course/introduction-to-machine-learning-using-r-and-rstudio-imlr02/> ONLINE COURSE – Introduction to Single Cell Analysis (ISCA01) This course will be delivered live <https://www.prstats.org/course/introduction-to-single-cell-analysis-isca01/>ONLINE COURSE – Using Google Earth Engine in Ecological Studies (GEEE01) This course will be delivered live <https://www.prstats.org/course/using-google-earth-engine-in-ecological-studies-geee01/>ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live <https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/> ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live <https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/>ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live <https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07)
ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/ <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr06/> *31st - March - 4th April 2025* Please feel free to share! We encourage attendees to bring their own data, you will receive opportunities to discuss your data with the instructor throughout the course, if you would like guideline on how to organize your data prior to the course please ask oliverhooo...@prstatistica.com This course is suitable for researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches, with applications to biogeography, spatial ecology, biodiversity conservation and related disciplines. This 5-day course will cover R concepts, methods, and tools that can be used to analyze community ecology data. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity indices, distance measures and distance-based multivariate methods, clustering, classification and ordination techniques using the R package VEGAN. We will use real-world empirical data sets to motivate analyses, such as describing patterns along gradients of environ-mental or anthropogenic disturbances, and quantifying the effects of continuous and discrete predictors. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures, guided computer coding, and participant exercises. The course is intended for intermediate users of R who are interested in community ecology, particularly in the areas of terrestrial and wetland ecology, microbial ecology, and natural resource management. You are strongly encouraged to use your own data sets (they should be clean and already structured, see the document: “recommendation if you participate with your data”. *Classes will run from 08:00 – 13:00 for the morning lecture and 14:00 – 16:00 for the practical (UK time) with an evening time session tbc for US, Canada etc. attendees. The course will be recorded and made available each day and will remain available for 28 days after the course for you to revisit any lectures.* DAY 1 • Module 1: Introduction to community data analysis, basics of programming in R • Module 2: Diversity analysis, species-abundance distributions DAY 2 • Module 3: Distance and transformation measures • Module 4: Clustering and classification analysis DAY 3 • Module 5: Unconstrained ordinations: Principal Component Analysis • Module 6: Other unconstrained ordinations DAY 4 • Module 7: Constrained ordinations: RDA and other canonical analysis • Module 8: Statistical tests for multivariate data and variation partitioning DAY 5 • Module 9: Overview of Spatial analysis, and recent Hierarchical Modeling of Species Communities (HMSC) methods • Modules 10: Special topics and discussion, analyzing participants’ data. Please email oliverhoo...@prstatistics.com with any questions -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Using Google Earth Engine in Ecological Studies (GEEE01)
ONLINE COURSE – Using Google Earth Engine in Ecological Studies (GEEE01) https://www.prstats.org/course/using-google-earth-engine-in-ecological-studies-geee01/ 9th - 13th December 2024 Please feel free to share! About this course; Google Earth Engine (GEE) is a cloud computing platform for processing satellite imagery and other geospatial and observational data. GEE is currently the most complete and efficient platform for performing remote sensing analysis, as it provides access to a large database of satellite imagery and the computational power needed to analyse these images. While other remote sensing programs require the user to have sufficient space and computing power available, all data and processes in GEE are done in the cloud through Google's infrastructure. GEE provides a code editor that works with JavaScript and Python. GEE is the future of remote sensing. By the end of the course, participants should: Know the catalogue of spatial datasets provided by GEE. Know the most important satellite sensors for environmental studies in GEE. Know how to get remote sensing products from GEE. Process and develop new remote sensing (sub-)products in GEE. Classify satellite imagery in GEE. Perform different types of spatial analyses in satellite imagery in GEE. Email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Machine Vision using Python (MVUP01)
ONLINE COURSE – Machine Vision using Python (MVUP01) This course will be delivered live https://www.prstats.org/course/machine-vision-using-python-mvup01/ February 3rd - 7th 2025 Please feel free to share! *ABOUT THIS COURSE.* Machine vision has produced many helpful image-processing techniques in several fields, such as object detection, classification, and segmentation. Machine vision is an interdisciplinary discipline combining computer vision and machine learning methods, mainly deep learning, to solve vision problems. Common problems, such as classification and localisation, are typical examples that combine these research fields. These techniques have applications in many areas. Deep learning methods are commonly applied for image classification, focusing on deep neural networks and Convolutional Neural Networks (CNNs), including concepts of transfer learning applied to image classification. This course introduces basic concepts of deep learning and machine vision applied to image classification using CNNs. To illustrate these methods, a dataset of medically and forensically important flies is used. Other examples will also be used during the course to illustrate the applications of machine vision in ecology. By the end of the course, participants should: - Understand the basic concepts behind the machine vision ecosystem in Python; - Understand the machine vision pipeline workflow; - Understand the application of standard Python packages such as OpenCV and Tensorflow; - Understand the basic concepts behind Deep Neural Networks; - Understand the basic concepts behind Convolutional Deep Neural Networks; - Understand basic concepts behind Transfer learning; - Have the confidence to implement basic Machine vision methods using Python; - Have the confidence to combine basic computer vision and machine learning methods to perform vision tasks; Please email oliverhoo...@prstatistics.com with any questions. *UPCOMING COURSES* ONLINE COURSE – Introduction to Single Cell Analysis (ISCA01) This course will be delivered live <https://www.prstats.org/course/introduction-to-single-cell-analysis-isca01/> ONLINE COURSE – Using Google Earth Engine in Ecological Studies (GEEE01) This course will be delivered live <https://www.prstats.org/course/using-google-earth-engine-in-ecological-studies-geee01/> ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live <https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/> ONLINE COURSE – Machine Vision using Python (MVUP01) This course will be delivered live <https://www.prstats.org/course/machine-vision-using-python-mvup01/> ONLINE COURSE – Machine Learning using Python (MLUP01) This course will be delivered live <https://www.prstats.org/course/machine-learning-using-python-mlup01/> ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live <https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/> ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live <https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> ONLINE COURSE – Bioacoustics Data Analysis using R (BIAC05) This course will be delivered live <https://www.prstats.org/course/bioacoustics-data-analysis-biac05/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Using Google Earth Engine in Ecological Studies (GEEE01)
ONLINE COURSE – Using Google Earth Engine in Ecological Studies (GEEE01) https://www.prstats.org/course/using-google-earth-engine-in-ecological-studies-geee01/ 20th - 24th January 2025 Please feel free to share! About this course; Google Earth Engine (GEE) is a cloud computing platform for processing satellite imagery and other geospatial and observational data. GEE is currently the most complete and efficient platform for performing remote sensing analysis, as it provides access to a large database of satellite imagery and the computational power needed to analyse these images. While other remote sensing programs require the user to have sufficient space and computing power available, all data and processes in GEE are done in the cloud through Google's infrastructure. GEE provides a code editor that works with JavaScript and Python. GEE is the future of remote sensing. By the end of the course, participants should: Know the catalogue of spatial datasets provided by GEE. Know the most important satellite sensors for environmental studies in GEE. Know how to get remote sensing products from GEE. Process and develop new remote sensing (sub-)products in GEE. Classify satellite imagery in GEE. Perform different types of spatial analyses in satellite imagery in GEE. Email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01)
ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/ Use discount code 'JAN25' to make the most of our Jan sale worth 20% off all courses Instructor - Dr. Rafael De Andrade Moral 27th Jan - 5th Feb 2025 Please feel free to share! In this six-day course (Approx. 35 hours), we provide a comprehensive practical and theoretical introduction to time series analysis and forecasting methods using R. Forecasting tools are useful in many areas, such as finance, meteorology, ecology, public policy, and health. We start by introducing the concepts of time series and stationarity, which will help us when studying ARIMA-type models. We will also cover autocorrelation functions and series decomposition methods. Then, we will introduce benchmark forecasting methods, namely the naïve (or random walk) method, mean, drift, and seasonal naïve methods. After that, we will present different exponential smoothing methods (simple, Holt’s linear method, and Holt-Winters seasonal method). We will then cover autoregressive integrated moving-average (or ARIMA) models, with and without seasonality. We will also cover Generalized Additive Models (GAMs) and how they can be used to incorporate seasonality effects in the analysis of time series data. Finally, we will cover Bayesian implementations of time series models and introduce extended models, such as ARCH, GARCH and stochastic volatility models, as well as Brownian motion and Ornstein-Uhlenbeck processes. Please email oliverhoo...@prstatistics.com with any questions. Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01)
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live https://www.prstats.org/course/phylogenetic-species-distribution-modelling-using-r-psdm01-25/ 12th - 14th May 2025 Instructor - Dr. Morales Castilla Ignacio Please feel free to share! COURSE OVERVIEW: In this three-day course, we introduce species distribution models (SDMs) and ways to incorporate phylogenetic information into single species models using R. We begin by providing an overview on the use of SDMs as a central tool for ecologists and evolutionary biologists, review and implement common SDM approaches and introduce hybrid models, which use the information in functional traits to complement the models. We then justify the rationale for using phylogenetic information in absence of functional trait data and show how to incorporate phylogenetic information in SDMs (day 1). We review examples of practical implementation of PSDMs to both present and future climate scenarios (day 2). Finally, we overview more advanced approaches of incorporating phylogenies into models (the Bayesian Phylogenetic Mixed Model) and how to project model results into a spatial context (day 3). Please email oliverhoo...@prstatistics.com with any questions. February ONLINE COURSE – Machine Vision using Python (MVUP01) This course will be delivered live <https://www.prstats.org/course/machine-vision-using-python-mvup01/> ONLINE COURSE – Machine Learning using Python (MLUP01) This course will be delivered live ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live <https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/> ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live <https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/> ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) This course will be delivered live <https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/> ONLINE COURSE – Community Analytics in Ecology and Evolutionary Biology for Beginners (CAFB01) This course will be delivered live <https://www.prstats.org/course/community-analytics-in-ecology-and-evolutionary-biology-for-beginners-cafb01/> March ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) This course will be delivered live <https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/> ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11) This course will be delivered live <https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> May ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered liveONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live June ONLINE COURSE – Tidyverse for Ecologists and Evolutionary Biologists (TIDY01) This course will be delivered live July ONLINE COURSE – Path analysis, structural equations and causal inference for biologists (PSCB03) October ONLINE COURSE – Bioacoustics Data Analysis using R (BIAC05) This course will be delivered live -- -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06)
We still have a few places on next week's course! ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/ Delivered by Dr. Marcia Barbosa 11th - 13th February 2025 Please feel free to share! This course focuses on the use of BART (Bayesian Additive Regression Trees) for modelling species’ geographical distributions based on occurrence data and environmental variables. BART is a relatively recent technique that shows very promising results in the field of species distribution and ecological niche modelling (SDM / ENM), as it produces accurate predictions (considering various aspects of model performance) without overfitting to noise or to special cases in the data. Additionally, BART allows mapping the uncertainty and credible intervals associated with eac local prediction. The course includes a combination of theoretical lectures and hands-on practicals in R, as well as open discussions about models and data for SDM applications. The practicals go through a complete worked example, from data preparation to model output analysis, with annotated R scripts that can be adapted on-the-spot by participants to work on their own species of interest. Along the course, the instructor is available for constant feedback and orientation on participants’; outputs and interpretations. Please email oliverhoo...@prstatistics.com with any questions -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Two Stats Courses on GLM's and Mixed Models
ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/ 26th - 27th February This course provides a comprehensive practical and theoretical introduction to generalized linear models using R. Generalized linear models are generalizations of linear regression models for situations where the outcome variable is, for example, a binary, or ordinal, or count variable, etc. The specific models we cover include binary, binomial, and categorical logistic regression, Poisson and negative binomial regression for count variables, as well as extensions for overdispersed and zero-inflated data. We begin by providing a brief overview of the normal general linear model. Understanding this model is vital for the proper understanding of how it is generalized in generalized linear models. Next, we introduce the widely used binary logistic regression model, which is is a regression model for when the outcome variable is binary. Next, we cover the binomial logistic regression, and the multinomial case, which is for modelling outcomes variables that are polychotomous, i.e., have more than two categorically distinct values. We will then cover Poisson regression, which is widely used for modelling outcome variables that are counts (i.e the number of times something has happened). We then cover extensions to accommodate overdispersion, starting with the quasi-likelihood approach, then covering the negative binomial and beta-binomial models for counts and discrete proportions, respectively. Finally, we will cover zero-inflated Poisson and negative binomial models, which are for count data with excessive numbers of zero observations. -- ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/ 11th - 13th March This course provides a comprehensive practical and theoretical introduction to multilevel models, also known as hierarchical or mixed effects models. We will focus primarily on multilevel linear models, but also cover multilevel generalized linear models. Likewise, we will also describe Bayesian approaches to multilevel modelling. We will begin by focusing on random effects multilevel models. These models make it clear how multilevel models are in fact models of models. In addition, random effects models serve as a solid basis for understanding mixed effects, i.e. fixed and random effects, models. In this coverage of random effects, we will also cover the important concepts of statistical shrinkage in the estimation of effects, as well as intraclass correlation. We then proceed to cover linear mixed effects models, particularly focusing on varying intercept and/or varying slopes regression models. We will then cover further aspects of linear mixed effects models, including multilevel models for nested and crossed data data, and group level predictor variables. Towards the end of the course we also cover generalized linear mixed models (GLMMs), how to accommodate overdispersion through individual-level random effects, as well as Bayesian approaches to multilevel levels using the brms R package. -- If you are booking both courses, please email me and I will provide a discount code for 10% off the total cost of both courses. Please email oliverhoo...@prstatistics.com with any questions. Please feel free to share among colleagues and fiends -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE - Remote sensing data analysis and coding in R for ecology (RSDA01)
ONLINE COURSE - Remote sensing data analysis and coding in R for ecology (RSDA01) https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/ We still have a few spaces on our Remote Sensing course in two weeks with Duccio Rocchini. 17th - 21st February 2025 Please feel free to share! COURSE OVERVIEW: Ecological remote sensing is now recognised as one of the founding disciplines to link spatial patterns to ecological changes in space and time. This course mainly focuses on the application of free and open source algorithms – which ensure high reproducibility and robustness of ecological analysis – to study ecological change in space and time by remotely sensed imagery. Particular emphasis will be given to: 1) remote sensing principles, 2) remotely sensed data gathering and analysis, 3) monitoring ecosystem change in space and time by remote sensing data. The course is dramatically practical giving space to exercises and additional ecological issues provided by the professor and suggested by students. We will make use of R which is one of the main free and open source software for ecological modelling. By the end of the course, participants will: • be able to create their own projects on monitoring of spatial and temporal changes of ecosystems with remote sensing data • be able to report in LaTeX and R Markdown the achieved results Please email oliverhoo...@prstatistics.com with any questions. February ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live <https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/> ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live <https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/> ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) This course will be delivered live <https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/> ONLINE COURSE – Community Analytics in Ecology and Evolutionary Biology for Beginners (CAFB01) This course will be delivered live <https://www.prstats.org/course/community-analytics-in-ecology-and-evolutionary-biology-for-beginners-cafb01/> March ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) This course will be delivered live <https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/> ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11) This course will be delivered live <https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> May ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live <https://www.prstats.org/course/phylogenetic-species-distribution-modelling-using-r-psdm01-25/> ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live <https://www.prstats.org/course/movement-ecology-using-rmove07/> June ONLINE COURSE – Tidyverse for Ecologists and Evolutionary Biologists (TIDY01) This course will be delivered live <https://www.prstats.org/course/online-course-tidyverse-for-ecologists-and-evolutionary-biologists-tidy01-this-course-will-be-delivered-live/> October ONLINE COURSE – Bioacoustics Data Analysis using R (BIAC05) This course will be delivered live <https://www.prstats.org/course/bioacoustics-data-analysis-biac05/> ONLINE COURSE – Path analysis, structural equations and causal inference for biologists (PSCB03) This course will be delivered live <https://www.prstats.org/course/path-analysis-structural-equations-and-causal-inference-for-biologists-pscb03/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01)
ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/ Instructor - Dr. Rafael De Andrade Moral 27th Jan - 5th Feb 2025 Please feel free to share! In this six-day course (Approx. 35 hours), we provide a comprehensive practical and theoretical introduction to time series analysis and forecasting methods using R. Forecasting tools are useful in many areas, such as finance, meteorology, ecology, public policy, and health. We start by introducing the concepts of time series and stationarity, which will help us when studying ARIMA-type models. We will also cover autocorrelation functions and series decomposition methods. Then, we will introduce benchmark forecasting methods, namely the naïve (or random walk) method, mean, drift, and seasonal naïve methods. After that, we will present different exponential smoothing methods (simple, Holt’s linear method, and Holt-Winters seasonal method). We will then cover autoregressive integrated moving-average (or ARIMA) models, with and without seasonality. We will also cover Generalized Additive Models (GAMs) and how they can be used to incorporate seasonality effects in the analysis of time series data. Finally, we will cover Bayesian implementations of time series models and introduce extended models, such as ARCH, GARCH and stochastic volatility models, as well as Brownian motion and Ornstein-Uhlenbeck processes. Please email oliverhoo...@prstatistics.com with any questions. ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live <https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/> ONLINE COURSE – Machine Vision using Python (MVUP01) This course will be delivered live <https://www.prstats.org/course/machine-vision-using-python-mvup01/> ONLINE COURSE – Machine Learning using Python (MLUP01) This course will be delivered live <https://www.prstats.org/course/machine-learning-using-python-mlup01/> ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live <https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/> ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live <https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/> ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) This course will be delivered live <https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/> ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) This course will be delivered live <https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/> ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11) This course will be delivered live <https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live <https://www.prstats.org/course/movement-ecology-using-rmove07/> ONLINE COURSE – Bioacoustics Data Analysis using R (BIAC05) This course will be delivered live <https://www.prstats.org/course/bioacoustics-data-analysis-biac05/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Machine Learning using Python (MLUP01)
ONLINE COURSE – Machine Learning using Python (MLUP01) This course will be delivered live https://www.prstats.org/course/machine-learning-using-python-mlup01/ February 10th - 14th 2025 Please feel free to share! *ABOUT THIS COURSE.* Machine vision has produced many helpful image-processing techniques in several fields, such as object detection, classification, and segmentation. Machine vision is an interdisciplinary discipline combining computer vision and machine learning methods, mainly deep learning, to solve vision problems. Common problems, such as classification and localisation, are typical examples that combine these research fields. These techniques have applications in many areas. Deep learning methods are commonly applied for image classification, focusing on deep neural networks and Convolutional Neural Networks (CNNs), including concepts of transfer learning applied to image classification. This course introduces basic concepts of deep learning and machine vision applied to image classification using CNNs. To illustrate these methods, a dataset of medically and forensically important flies is used. Other examples will also be used during the course to illustrate the applications of machine vision in ecology. By the end of the course, participants should: - Understand the basic concepts behind the machine vision ecosystem in Python; - Understand the machine vision pipeline workflow; - Understand the application of standard Python packages such as OpenCV and Tensorflow; - Understand the basic concepts behind Deep Neural Networks; - Understand the basic concepts behind Convolutional Deep Neural Networks; - Understand basic concepts behind Transfer learning; - Have the confidence to implement basic Machine vision methods using Python; - Have the confidence to combine basic computer vision and machine learning methods to perform vision tasks; Please email oliverhoo...@prstatistics.com with any questions. *UPCOMING COURSES* ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live <https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/> ONLINE COURSE – Machine Vision using Python (MVUP01) This course will be delivered live <https://www.prstats.org/course/machine-vision-using-python-mvup01/> ONLINE COURSE – Machine Learning using Python (MLUP01) This course will be delivered live <https://www.prstats.org/course/machine-learning-using-python-mlup01/> ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live <https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/> ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live <https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> ONLINE COURSE – Bioacoustics Data Analysis using R (BIAC05) This course will be delivered live <https://www.prstats.org/course/bioacoustics-data-analysis-biac05/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE - Movement Ecology Using R (MOVE06)
ONLINE COURSE - Movement Ecology Using R (MOVE06) https://www.prstats.org/course/online-course-movement-ecology-move06/ Delivered by Prof. Luca Borga and Prof. Garrett Street 13th - 17th May 2024 Please feel free to share! This is a fully live course with two instrcutors alwys availble during lectures and practicals so you will alwys have support on hand. We alsorecord all live sessions to accommodate different time zones. All attendees will have access to recordings for a further 3 months after the course to revisit any of the classes. COURSE OVERVIEW - The course will cover the concepts, technology and software tools that can be used to analyse movement data (from ringing/CMR to VHF/GPS) in ecology and evolution. We will cover elementary and advanced analysis and modelling techniques broadly applicable across taxa, from micro-organisms to vertebrates, highlighting the advantages of a unified Movement Ecology framework. We will provide the necessary bases in ecology (especially behavioural ecology), physics and mathematics/statistics, to be able to identify for any specific research question the most appropriate study species, logging technology (incl. attachment methods), and statistical/mathematical modelling approach. We will specifically address the challenges and opportunities at each of the steps of the proposed ‘question-driven approach’, combining theory with computer-based practicals in R. We will also address the challenges of applying the results of the analyses to applied management problems and communicate the findings to non-experts. Please email oliverhoo...@prstatistics.com with any questions ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live <https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/> ONLINE COURSE – Machine Vision using Python (MVUP01) This course will be delivered live <https://www.prstats.org/course/machine-vision-using-python-mvup01/> ONLINE COURSE – Machine Learning using Python (MLUP01) This course will be delivered live <https://www.prstats.org/course/machine-learning-using-python-mlup01/> ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live <https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/> ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live <https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/> ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) This course will be delivered live <https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/> ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) This course will be delivered live <https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/> ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11) This course will be delivered live <https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live <https://www.prstats.org/course/movement-ecology-using-rmove07/> ONLINE COURSE – Bioacoustics Data Analysis using R (BIAC05) This course will be delivered live <https://www.prstats.org/course/bioacoustics-data-analysis-biac05/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08)
ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/ 25th - 27th February 2025 Instructor - Dr. Rafael De Andrade Moral COURSE OVERVIEW: This course provides a comprehensive practical and theoretical introduction to generalized linear models using R. Generalized linear models are generalizations of linear regression models for situations where the outcome variable is, for example, a binary, or ordinal, or count variable, etc. The specific models we cover include binary, binomial, and categorical logistic regression, Poisson and negative binomial regression for count variables, as well as extensions for overdispersed and zero-inflated data. We begin by providing a brief overview of the normal general linear model. Understanding this model is vital for the proper understanding of how it is generalized in generalized linear models. Next, we introduce the widely used binary logistic regression model, which is is a regression model for when the outcome variable is binary. Next, we cover the binomial logistic regression, and the multinomial case, which is for modelling outcomes variables that are polychotomous, i.e., have more than two categorically distinct values. We will then cover Poisson regression, which is widely used for modelling outcome variables that are counts (i.e the number of times something has happened). We then cover extensions to accommodate overdispersion, starting with the quasi-likelihood approach, then covering the negative binomial and beta-binomial models for counts and discrete proportions, respectively. Finally, we will cover zero-inflated Poisson and negative binomial models, which are for count data with excessive numbers of zero observations. Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE - Remote sensing data analysis and coding in R for ecology (RSDA01)
ONLINE COURSE - Remote sensing data analysis and coding in R for ecology (RSDA01) https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/ 17th - 21st February 2025 Instructor - Duccio Rocchini COURSE OVERVIEW: Ecological remote sensing is now recognised as one of the founding disciplines to link spatial patterns to ecological changes in space and time. This course mainly focuses on the application of free and open source algorithms – which ensure high reproducibility and robustness of ecological analysis – to study ecological change in space and time by remotely sensed imagery. Particular emphasis will be given to: 1) remote sensing principles, 2) remotely sensed data gathering and analysis, 3) monitoring ecosystem change in space and time by remote sensing data. The course is dramatically practical giving space to exercises and additional ecological issues provided by the professor and suggested by students. We will make use of R which is one of the main free and open source software for ecological modelling. By the end of the course, participants will: • be able to create their own projects on monitoring of spatial and temporal changes of ecosystems with remote sensing data • be able to report in LaTeX and R Markdown the achieved results Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06)
ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/ Delivered by Dr. Marcia Barbosa 11th - 13th February 2025 Please feel free to share! ABOUT THIS COURSE This course focuses on the use of BART (Bayesian Additive Regression Trees) for modelling species’ geographical distributions based on occurrence data and environmental variables. BART is a relatively recent technique that shows very promising results in the field of species distribution and ecological niche modelling (SDM / ENM), as it produces accurate predictions (considering various aspects of model performance) without overfitting to noise or to special cases in the data. Additionally, BART allows mapping the uncertainty and credible intervals associated with eac local prediction. The course includes a combination of theoretical lectures and hands-on practicals in R, as well asopen discussions about models and data for SDM applications. The practicals go through acomplete worked example, from data preparation to model output analysis, with annotated Rscripts that can be adapted on-the-spot by participants to work on their own species of interest. Along the course, the instructor is available for constant feedback and orientation on participants’; outputs and interpretations. Please email oliverhoo...@prstatistics.com with any questions ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live <https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/> ONLINE COURSE – Machine Vision using Python (MVUP01) This course will be delivered live <https://www.prstats.org/course/machine-vision-using-python-mvup01/> ONLINE COURSE – Machine Learning using Python (MLUP01) This course will be delivered live <https://www.prstats.org/course/machine-learning-using-python-mlup01/> ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live <https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/> ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live <https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/> ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) This course will be delivered live <https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/> ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) This course will be delivered live <https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/> ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11) This course will be delivered live <https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live <https://www.prstats.org/course/movement-ecology-using-rmove07/> ONLINE COURSE – Bioacoustics Data Analysis using R (BIAC05) This course will be delivered live <https://www.prstats.org/course/bioacoustics-data-analysis-biac05/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11)
ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11) https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/ 25th - 28th March 2025 Instructor - Dr. Andrew Jackson COURSE OVERVIEW: This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. Recently SIMMs have become a very popular tool for quantifying food webs and thus the diet of predators and prey in an ecosystem. Starting with only basic understanding of statistical models, we will cover the do’s and don’ts of using SIMMs with a particular focus on the widely used package SIAR and the more advanced MixSIAR. Participants will be taught some of the advanced features of these packages, which will enable them to produce a richer class of output, and are encouraged to bring their own data sets and problems to study during the round-table discussions. DAY 1 Basic concepts. Module 1: Introduction; why use a SIMM? Module 2: An introduction to Bayesian statistics. Module 3: Differences between regression models and SIMMs. Practical: Revision on using R to load data, create plots and fit statistical models. Round table discussion: Understanding the output from a Bayesian model. DAY 2 Understanding and using SIAR. Module 4: Do’s and Don’ts of using SIAR. Module 5: The statistical model behind SIAR. Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots. Round table discussion: Issues when using simple SIMMs. DAY 3 SIBER and MixSIAR. Module 6: Creating and understanding Stable Isotope Bayesian Ellipses (SIBER). Module 7: What are the differences between SIAR and MixSIAR? Practical: Using MixSIAR on real world data sets; benefits over SIAR. Round table discussion: When to use which type of SIMM. DAY 4 Advanced SIMMs. Module 8: Using MixSIAR for complex data sets: time series and mixed effects models. Module 9: Source grouping: when and how? Module 10: Building your own SIMM with JAGS. Practical: Running advanced SIMMs with JAGS. Round table discussion: Bring your own data set. Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09)
ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/ 11th - 13th march 2025 Instructor - Dr. Rafael De Andrade Moral COURSE OVERVIEW: This course provides a comprehensive practical and theoretical introduction to multilevel models, also known as hierarchical or mixed effects models. We will focus primarily on multilevel linear models, but also cover multilevel generalized linear models. Likewise, we will also describe Bayesian approaches to multilevel modelling. We will begin by focusing on *random effects* multilevel models. These models make it clear how multilevel models are in fact models of models. In addition, random effects models serve as a solid basis for understanding mixed effects, i.e. fixed and random effects, models. In this coverage of random effects, we will also cover the important concepts of statistical shrinkage in the estimation of effects, as well as intraclass correlation. We then proceed to cover linear mixed effects models, particularly focusing on varying intercept and/or varying slopes regression models. We will then cover further aspects of linear mixed effects models, including multilevel models for nested and crossed data data, and group level predictor variables. Towards the end of the course we also cover generalized linear mixed models (GLMMs), how to accommodate overdispersion through individual-level random effects, as well as Bayesian approaches to multilevel levels using the brms R package. Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01)
ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/ Final call - we still have a few places left on next week's course! Use discount code 'JAN25' to make the most of our Jan sale worth 20% off all courses Instructor - Dr. Rafael De Andrade Moral 27th Jan - 5th Feb 2025 Please feel free to share! In this six-day course (Approx. 35 hours), we provide a comprehensive practical and theoretical introduction to time series analysis and forecasting methods using R. Forecasting tools are useful in many areas, such as finance, meteorology, ecology, public policy, and health. We start by introducing the concepts of time series and stationarity, which will help us when studying ARIMA-type models. We will also cover autocorrelation functions and series decomposition methods. Then, we will introduce benchmark forecasting methods, namely the naïve (or random walk) method, mean, drift, and seasonal naïve methods. After that, we will present different exponential smoothing methods (simple, Holt’s linear method, and Holt-Winters seasonal method). We will then cover autoregressive integrated moving-average (or ARIMA) models, with and without seasonality. We will also cover Generalized Additive Models (GAMs) and how they can be used to incorporate seasonality effects in the analysis of time series data. Finally, we will cover Bayesian implementations of time series models and introduce extended models, such as ARCH, GARCH and stochastic volatility models, as well as Brownian motion and Ornstein-Uhlenbeck processes. Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Machine Learning using Python (MLUP01)
We still have places on the upcoming course "Machine Learning using Python (MLUP01)" https://www.prstats.org/course/machine-learning-using-python-mlup01/ Use discount code 'JAN25' to make the most of our January sale worth 20% off all courses February 10th - 14th 2025 Please feel free to share! *ABOUT THIS COURSE.* Machine vision has produced many helpful image-processing techniques in several fields, such as object detection, classification, and segmentation. Machine vision is an interdisciplinary discipline combining computer vision and machine learning methods, mainly deep learning, to solve vision problems. Common problems, such as classification and localisation, are typical examples that combine these research fields. These techniques have applications in many areas. Deep learning methods are commonly applied for image classification, focusing on deep neural networks and Convolutional Neural Networks (CNNs), including concepts of transfer learning applied to image classification. This course introduces basic concepts of deep learning and machine vision applied to image classification using CNNs. To illustrate these methods, a dataset of medically and forensically important flies is used. Other examples will also be used during the course to illustrate the applications of machine vision in ecology. By the end of the course, participants should: - Understand the basic concepts behind the machine vision ecosystem in Python; - Understand the machine vision pipeline workflow; - Understand the application of standard Python packages such as OpenCV and Tensorflow; - Understand the basic concepts behind Deep Neural Networks; - Understand the basic concepts behind Convolutional Deep Neural Networks; - Understand basic concepts behind Transfer learning; - Have the confidence to implement basic Machine vision methods using Python; - Have the confidence to combine basic computer vision and machine learning methods to perform vision tasks; Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Tidyverse for Ecologists (TIDY01)
ONLINE COURSE – Tidyverse for Ecologists (TIDY01) https://www.prstats.org/course/tidyverse-for-ecologists-tidy01/ 16th - 20th June Please feel free to share! COURSE OVERVIEW - This course comprehensively introduces the Tidyverse and focuses on its use in data science projects. It is designed to give participants a strong foundation in R programming, core Tidyverse packages, and the Tidymodels framework. The course emphasises hands-on projects to apply learned concepts to real-world data analysis and modelling tasks applied to biology. By the end of the course, participants should: Understand the fundamentals of R programming for data analysis. Be proficient in using core Tidyverse packages to clean, transform, and visualise data. Gain an introduction to basic machine learning concepts through the Tidymodels framework. Learn to preprocess, build, evaluate, and interpret models using Tidymodels. Apply Tidyverse and Tidymodels tools to solve real-world problems through hands-on projects. Please email oliverhoo...@prstatistics.com with any questions Day 1: A Short Course in R Basics (9:30 - 17:30) This day provides participants with the foundational R skills required for working with Tidyverse and Tidymodels. It is designed for beginners or those needing a refresher in R programming. Section 1 (R Essentials): This section focuses on R syntax, variables, data types, conditionals (`if`, `else`, `elif`), loops (`for`, `while`), and writing reusable code using functions. Section 2 (Data Structures and File Handling in R): This section emphasises understanding data structures (e.g., vectors, data frames, lists) and handling files by reading/writing data (e.g., CSVs) for manipulation and analysis. Day 2: Fundamentals of Tidyverse I (9:30 - 17:30) This day introduces participants to the foundational concepts of Tidyverse packages and their applications to data science projects. Section 3 (Data Manipulation I): This section covers the basics of data manipulation using `dplyr` functions such as `filter()`, `select()`, `mutate()`, `arrange()`, and `summarise ()`. Participants will learn how to clean, transform, and prepare datasets for analysis. Section 4 (Data Visualisation I): This section introduces the principles of data visualisation using `ggplot2`. Participants will learn how to create basic plots such as scatterplots, bar charts, and line graphs while exploring the grammar of graphics. Day 3: Fundamentals of Tidyverse II (9:30 - 17:30) This day builds on the foundations established in Day 2 and dives deeper into advanced data manipulation and visualisation techniques. Section 5 (Data Manipulation II): This section extends the use of `dplyr` by introducing more complex operations such as joins, grouping with `group_by()`, and working with pipelines using `%>%`. Finally, additional packages will be presented to enhance data manipulation programming. Section 6 (Data Visualisation II): Participants will explore advanced visualisation techniques using extensions of `ggplot2`, such as creating animated plots with the `gganimate` package and interactive visualisations with additional tools. Day 4: Applying Tidyverse Fundamentals to Data Modelling (9:30 - 17:30) This day introduces participants to machine learning concepts using core libraries for statistical modelling and deep learning. Section 7 (Introduction to regression): This section focuses on regression modelling using Tidymodels. Participants will learn to implement linear regression models, evaluate model performance, and interpret results. Section 8 (Introduction to Classification): This section introduces techniques such as support vector machines and neural networks using Tidymodels. Participants will also explore methods for assessing the performance of classification models. Day 5: Data Science Workflow with Tidyverse (9:30 - 17:30) On the final day, participants will apply all their newly acquired skills to solve real-world problems inspired by ecological datasets. Section 9 (The data science workflow): The workflow will be illustrated based on the core packages introduced. The book "R for Data Science" will serve as a base literature for this day Section 10 (Hands-on project): Participants will work through a complete data science workflow, including data cleaning, transformation, visualisation, modelling, and communication of results. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07)
o (IMMR09) This course will be delivered live <https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/> ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11) This course will be delivered live <https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live <https://www.prstats.org/course/movement-ecology-using-rmove07/> ONLINE COURSE – Bioacoustics Data Analysis using R (BIAC05) This course will be delivered live <https://www.prstats.org/course/bioacoustics-data-analysis-biac05/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11)
ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11) https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/ Use discount code 'JAN25' to make the most of our Jan sale worth 20% off all courses 25th - 28th March 2025 Instructor - Dr. Andrew Jackson COURSE OVERVIEW: This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. Recently SIMMs have become a very popular tool for quantifying food webs and thus the diet of predators and prey in an ecosystem. Starting with only basic understanding of statistical models, we will cover the do’s and don’ts of using SIMMs with a particular focus on the widely used package SIAR and the more advanced MixSIAR. Participants will be taught some of the advanced features of these packages, which will enable them to produce a richer class of output, and are encouraged to bring their own data sets and problems to study during the round-table discussions. DAY 1 Basic concepts. Module 1: Introduction; why use a SIMM? Module 2: An introduction to Bayesian statistics. Module 3: Differences between regression models and SIMMs. Practical: Revision on using R to load data, create plots and fit statistical models. Round table discussion: Understanding the output from a Bayesian model. DAY 2 Understanding and using SIAR. Module 4: Do’s and Don’ts of using SIAR. Module 5: The statistical model behind SIAR. Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots. Round table discussion: Issues when using simple SIMMs. DAY 3 SIBER and MixSIAR. Module 6: Creating and understanding Stable Isotope Bayesian Ellipses (SIBER). Module 7: What are the differences between SIAR and MixSIAR? Practical: Using MixSIAR on real world data sets; benefits over SIAR. Round table discussion: When to use which type of SIMM. DAY 4 Advanced SIMMs. Module 8: Using MixSIAR for complex data sets: time series and mixed effects models. Module 9: Source grouping: when and how? Module 10: Building your own SIMM with JAGS. Practical: Running advanced SIMMs with JAGS. Round table discussion: Bring your own data set. Please email oliverhoo...@prstatistics.com with any questions. Upcoming courses ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live <https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/> ONLINE COURSE – Machine Vision using Python (MVUP01) This course will be delivered live <https://www.prstats.org/course/machine-vision-using-python-mvup01/> ONLINE COURSE – Machine Learning using Python (MLUP01) This course will be delivered live <https://www.prstats.org/course/machine-learning-using-python-mlup01/> ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live <https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/> ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live <https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/> ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) This course will be delivered live <https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/> ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) This course will be delivered live <https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/> ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11) This course will be delivered live <https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live <https://www.prstats.org/course/movement-ecology-using-rmove07/> ONLINE COURSE – Bioacoustics Data Analysis using R (BIAC05) This course will be delivered live <https://www.prstats.org/course/bioacoustics-data-analysis-biac05/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08)
ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/ Use discount code 'JAN25' to make the most of our Jan sale worth 20% off all courses 25th - 27th February 2025 Instructor - Dr. Rafael De Andrade Moral COURSE OVERVIEW: This course provides a comprehensive practical and theoretical introduction to generalized linear models using R. Generalized linear models are generalizations of linear regression models for situations where the outcome variable is, for example, a binary, or ordinal, or count variable, etc. The specific models we cover include binary, binomial, and categorical logistic regression, Poisson and negative binomial regression for count variables, as well as extensions for overdispersed and zero-inflated data. We begin by providing a brief overview of the normal general linear model. Understanding this model is vital for the proper understanding of how it is generalized in generalized linear models. Next, we introduce the widely used binary logistic regression model, which is is a regression model for when the outcome variable is binary. Next, we cover the binomial logistic regression, and the multinomial case, which is for modelling outcomes variables that are polychotomous, i.e., have more than two categorically distinct values. We will then cover Poisson regression, which is widely used for modelling outcome variables that are counts (i.e the number of times something has happened). We then cover extensions to accommodate overdispersion, starting with the quasi-likelihood approach, then covering the negative binomial and beta-binomial models for counts and discrete proportions, respectively. Finally, we will cover zero-inflated Poisson and negative binomial models, which are for count data with excessive numbers of zero observations. Please email oliverhoo...@prstatistics.com with any questions. January ONLINE COURSE – Using Google Earth Engine in Ecological Studies (GEEE01) This course will be delivered live <https://www.prstats.org/course/using-google-earth-engine-in-ecological-studies-geee01/> ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live <https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/> February ONLINE COURSE – Machine Vision using Python (MVUP01) This course will be delivered live <https://www.prstats.org/course/machine-vision-using-python-mvup01/> ONLINE COURSE – Machine Learning using Python (MLUP01) This course will be delivered live <https://www.prstats.org/course/machine-learning-using-python-mlup01/> ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live <https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/> ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live <https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/> ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) This course will be delivered live <https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/> ONLINE COURSE – Community Analytics in Ecology and Evolutionary Biology for Beginners (CAFB01) This course will be delivered live <https://www.prstats.org/course/community-analytics-in-ecology-and-evolutionary-biology-for-beginners-cafb01/> March ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) This course will be delivered live <https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/> ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11) This course will be delivered live <https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> May ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live <https://www.prstats.org/course/movement-ecology-using-rmove07/> June ONLINE COURSE – Tidyverse for Ecologists and Evolutionary Biologists (TIDY01) This course will be delivered live <https://www.prstats.org/course/online-course-tidyverse-for-ecologists-and-evolutionary-biologists-tidy01-this-course-will-be-delivered-live/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Community Analytics in Ecology and Evolutionary Biology for Beginners (CAFB01)
ONLINE COURSE – Community Analytics in Ecology and Evolutionary Biology for Beginners (CAFB01) https://www.prstats.org/course/community-analytics-in-ecology-and-evolutionary-biology-for-beginners-cafb01/ Use discount code 'JAN25' to make the most of our Jan sale worth 20% off all courses 26th - 28th February 2025 Instructor - Dr. Antoine Becker-Scarpitta COURSE OVERVIEW: This community analytics course is designed for students who have recently started their projects or researchers who are starting using the R ecosystem. During this three-day course, we will cover the basic concepts of multivariate analysis and their implementation in R. This course is a complement to the PR Statistic offering allowing also beginners and non-programmers to discover the statistical tools needed to analyze an ecological dataset in research, natural resource management or conservation context. This course is not geared toward any particular taxonomic group or ecological system. We will cover diversity indices, distance measures and multivariate distance-based methods, clustering, classification, and ordination techniques. We will focus on the concept of the methods and their implementation on R using different R packages. We will use real-world examples to implement analyses, such as describing patterns along gradients of environmental or anthropogenic disturbances, quantifying the effects of continuous and discrete predictors, data mining. The course will consist of lectures, work on R code scripts, and exercises for participants. PR stats also deliver a more advanced course on analysing community data ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) Please email oliverhoo...@prstatistics.com with any questions. January ONLINE COURSE – Using Google Earth Engine in Ecological Studies (GEEE01) This course will be delivered live <https://www.prstats.org/course/using-google-earth-engine-in-ecological-studies-geee01/> ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live <https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/> February ONLINE COURSE – Machine Vision using Python (MVUP01) This course will be delivered live <https://www.prstats.org/course/machine-vision-using-python-mvup01/> ONLINE COURSE – Machine Learning using Python (MLUP01) This course will be delivered live <https://www.prstats.org/course/machine-learning-using-python-mlup01/> ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live <https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/> ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live <https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/> ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) This course will be delivered live <https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/> ONLINE COURSE – Community Analytics in Ecology and Evolutionary Biology for Beginners (CAFB01) This course will be delivered live <https://www.prstats.org/course/community-analytics-in-ecology-and-evolutionary-biology-for-beginners-cafb01/> March ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) This course will be delivered live <https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/> ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11) This course will be delivered live <https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> May ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live <https://www.prstats.org/course/movement-ecology-using-rmove07/> June ONLINE COURSE – Tidyverse for Ecologists and Evolutionary Biologists (TIDY01) This course will be delivered live <https://www.prstats.org/course/online-course-tidyverse-for-ecologists-and-evolutionary-biologists-tidy01-this-course-will-be-delivered-live/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L PR stats have a January sale!
PR stats have a January sale! Any bookings made during January on live courses https://www.prstats.org/live-courses/ or recorded courses https://www.prstats.org/recorded-courses/ qualify for a 20% discount, use discount code JAN25. Happy new year!! Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09)
ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/ Use discount code 'JAN25' to make the most of our Jan sale worth 20% off all courses 11th - 13th march 2025 Instructor - Dr. Rafael De Andrade Moral COURSE OVERVIEW: This course provides a comprehensive practical and theoretical introduction to multilevel models, also known as hierarchical or mixed effects models. We will focus primarily on multilevel linear models, but also cover multilevel generalized linear models. Likewise, we will also describe Bayesian approaches to multilevel modelling. We will begin by focusing on *random effects* multilevel models. These models make it clear how multilevel models are in fact models of models. In addition, random effects models serve as a solid basis for understanding mixed effects, i.e. fixed and random effects, models. In this coverage of random effects, we will also cover the important concepts of statistical shrinkage in the estimation of effects, as well as intraclass correlation. We then proceed to cover linear mixed effects models, particularly focusing on varying intercept and/or varying slopes regression models. We will then cover further aspects of linear mixed effects models, including multilevel models for nested and crossed data data, and group level predictor variables. Towards the end of the course we also cover generalized linear mixed models (GLMMs), how to accommodate overdispersion through individual-level random effects, as well as Bayesian approaches to multilevel levels using the brms R package. Please email oliverhoo...@prstatistics.com with any questions. January *ONLINE COURSE – Using Google Earth Engine in Ecological Studies (GEEE01) This course will be delivered live* <https://www.prstats.org/course/using-google-earth-engine-in-ecological-studies-geee01/> *ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live* <https://www.prstats.org/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/> February *ONLINE COURSE – Machine Vision using Python (MVUP01) This course will be delivered live* <https://www.prstats.org/course/machine-vision-using-python-mvup01/> *ONLINE COURSE – Machine Learning using Python (MLUP01) This course will be delivered live* <https://www.prstats.org/course/machine-learning-using-python-mlup01/> ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics Using R (SDMB06) This course will be delivered live <https://www.prstats.org/course/online-course-species-distribution-modelling-with-bayesian-statistics-using-r-sdmb06/> *ONLINE COURSE – Remote sensing data analysis and coding in R for ecology (RSDA01) This course will be delivered live* <https://www.prstats.org/course/remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsda01/> ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) This course will be delivered live <https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/> *ONLINE COURSE – Community Analytics in Ecology and Evolutionary Biology for Beginners (CAFB01) This course will be delivered live* <https://www.prstats.org/course/community-analytics-in-ecology-and-evolutionary-biology-for-beginners-cafb01/> March ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) This course will be delivered live <https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/> ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11) This course will be delivered live <https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/> ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) This course will be delivered live <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/> May ONLINE COURSE – Movement Ecology Using R(MOVE07) This course will be delivered live <https://www.prstats.org/course/movement-ecology-using-rmove07/> June *ONLINE COURSE – Tidyverse for Ecologists and Evolutionary Biologists (TIDY01) This course will be delivered live* <https://www.prstats.org/course/online-course-tidyverse-for-ecologists-and-evolutionary-biologists-tidy01-this-course-will-be-delivered-live/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07)
ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) Only 3 places left! https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/ <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr06/> *31st - March - 4th April 2025* Please feel free to share! We encourage attendees to bring their own data, you will receive opportunities to discuss your data with the instructor throughout the course, if you would like guideline on how to organize your data prior to the course please ask oliverhooo...@prstatistica.com This course is suitable for researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches, with applications to biogeography, spatial ecology, biodiversity conservation and related disciplines. This 5-day course will cover R concepts, methods, and tools that can be used to analyze community ecology data. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity indices, distance measures and distance-based multivariate methods, clustering, classification and ordination techniques using the R package VEGAN. We will use real-world empirical data sets to motivate analyses, such as describing patterns along gradients of environ-mental or anthropogenic disturbances, and quantifying the effects of continuous and discrete predictors. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures, guided computer coding, and participant exercises. The course is intended for intermediate users of R who are interested in community ecology, particularly in the areas of terrestrial and wetland ecology, microbial ecology, and natural resource management. You are strongly encouraged to use your own data sets (they should be clean and already structured, see the document: “recommendation if you participate with your data”. *Classes will run from 08:00 – 13:00 for the morning lecture and 14:00 – 16:00 for the practical (UK time) with an evening time session tbc for US, Canada etc. attendees. The course will be recorded and made available each day and will remain available for 28 days after the course for you to revisit any lectures.* DAY 1 • Module 1: Introduction to community data analysis, basics of programming in R • Module 2: Diversity analysis, species-abundance distributions DAY 2 • Module 3: Distance and transformation measures • Module 4: Clustering and classification analysis DAY 3 • Module 5: Unconstrained ordinations: Principal Component Analysis • Module 6: Other unconstrained ordinations DAY 4 • Module 7: Constrained ordinations: RDA and other canonical analysis • Module 8: Statistical tests for multivariate data and variation partitioning DAY 5 • Module 9: Overview of Spatial analysis, and recent Hierarchical Modeling of Species Communities (HMSC) methods • Modules 10: Special topics and discussion, analyzing participants’ data. Email oliverghoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11)
*ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11)* *We still have some places on next week's Stable Isotope Mixing Models course Tuesday - Thursday!* https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/ 25th - 28th March 2025 Instructor - Dr. Andrew Jackson COURSE OVERVIEW: This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. Recently SIMMs have become a very popular tool for quantifying food webs and thus the diet of predators and prey in an ecosystem. Starting with only basic understanding of statistical models, we will cover the do’s and don’ts of using SIMMs with a particular focus on the widely used package SIAR and the more advanced MixSIAR. Participants will be taught some of the advanced features of these packages, which will enable them to produce a richer class of output, and are encouraged to bring their own data sets and problems to study during the round-table discussions. DAY 1 Basic concepts. Module 1: Introduction; why use a SIMM? Module 2: An introduction to Bayesian statistics. Module 3: Differences between regression models and SIMMs. Practical: Revision on using R to load data, create plots and fit statistical models. Round table discussion: Understanding the output from a Bayesian model. DAY 2 Understanding and using SIAR. Module 4: Do’s and Don’ts of using SIAR. Module 5: The statistical model behind SIAR. Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots. Round table discussion: Issues when using simple SIMMs. DAY 3 SIBER and MixSIAR. Module 6: Creating and understanding Stable Isotope Bayesian Ellipses (SIBER). Module 7: What are the differences between SIAR and MixSIAR? Practical: Using MixSIAR on real world data sets; benefits over SIAR. Round table discussion: When to use which type of SIMM. DAY 4 Advanced SIMMs. Module 8: Using MixSIAR for complex data sets: time series and mixed effects models. Module 9: Source grouping: when and how? Module 10: Building your own SIMM with JAGS. Practical: Running advanced SIMMs with JAGS. Round table discussion: Bring your own data set. Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Introduction To Mixed Models Using R And Rstudio (IMMR09)
ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/ 27th - 29th May 2025 This course provides a comprehensive practical and theoretical introduction to multilevel models, also known as hierarchical or mixed effects models. We will focus primarily on multilevel linear models, but also cover multilevel generalized linear models. Likewise, we will also describe Bayesian approaches to multilevel modelling. We will begin by focusing on random effects multilevel models. These models make it clear how multilevel models are in fact models of models. In addition, random effects models serve as a solid basis for understanding mixed effects, i.e. fixed and random effects, models. In this coverage of random effects, we will also cover the important concepts of statistical shrinkage in the estimation of effects, as well as intraclass correlation. We then proceed to cover linear mixed effects models, particularly focusing on varying intercept and/or varying slopes regression models. We will then cover further aspects of linear mixed effects models, including multilevel models for nested and crossed data data, and group level predictor variables. Towards the end of the course we also cover generalized linear mixed models (GLMMs), how to accommodate overdispersion through individual-level random effects, as well as Bayesian approaches to multilevel levels using the brms R package. *Day 1* Topic 1: Random effects models. The defining feature of multilevel models is that they are models of models. We begin by using a binomial random effects model to illustrate this. Specifically, we show how multilevel models are models of the variability in models of different clusters or groups of data. Topic 2: Normal random effects models. Normal, as in normal distribution, random effects models are the key to understanding the more general and widely used linear mixed effects models. Here, we also cover the key concepts of statistical shrinkage and intraclass correlation. *Day 2* Topic 3: Linear mixed effects models. Next, we turn to multilevel linear models, also known as linear mixed effects models. We specifically deal with the cases of varying intercept and/or varying slope linear regression models. Topic 4: Multilevel models for nested data. Here, we will consider multilevel linear models for nested, as in groups of groups, data. As an example, we will look at multilevel linear models applied to data from students within classes that are themselves within different schools, and where we model the variability of effects across the classes and across the schools. Topic 5: Multilevel models for crossed data. In some multilevel models, each observation occurs in multiple groups, but these groups are not nested. For example, animals may be members of different species and in different locations, but the species are not subsets of locations, nor vice versa. These are known as crossed or multiclass data structures. *Day 3* Topic 6: Group level predictors. In some multilevel regression models, predictor variable are sometimes associated with individuals, and sometimes associated with their groups. In this section, we consider how to handle these two situations. Topic 7: Generalized linear mixed models (GLMMs). Here, we extend the linear mixed model to the exponential family of distributions and showcase an example using the Poisson GLMM. We also cover how to accommodate overdispersion through individual-level random effects. Topic 8: Bayesian multilevel models. All of the models that we have considered can be handled, often more easily, using Bayesian models. Here, we provide an brief introduction to Bayesian models and how to perform examples of the models that we have considered using Bayesian methods and the brms R package. Please email oliverhoo...@prstatistics.com with any questions. Please feel free to share among colleagues and friends -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L NEW ON DEMAND COURSE - Remote Sensing Data Analysis and Coding in R for Ecologists (RSDAPR)
You can now follow our Remote Sensing course on demand allowing you to work at your own pace. You have access for 28 days and instructor support via email. Remote Sensing Data Analysis and Coding in R for Ecologists (RSDAPR) https://www.prstats.org/course/online-course-remote-sensing-data-analysis-and-coding-in-r-for-ecology-rsdapr/ Ecological remote sensing is now recognised as one of the founding disciplines to link spatial patterns to ecological changes in space and time. This course mainly focuses on the application of free and open source algorithms – which ensure high reproducibility and robustness of ecological analysis – to study ecological change in space and time by remotely sensed imagery. Particular emphasis will be given to: 1) remote sensing principles, 2) remotely sensed data gathering and analysis, 3) monitoring ecosystem change in space and time by remote sensing data. The course is dramatically practical giving space to exercises and additional ecological issues provided by the professor and suggested by students. We will make use of R which is one of the main free and open source software for ecological modelling. Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Do you want to learn Bayesian Statistics at your own pace - check out on demand courses!
Do you want to learn Bayesian Statistics at your own pace? If so check out on demand courses! You have access for 28 days and instructor support via email. Please feel free to share. Introduction / Fundamentals Of Bayesian Data Analysis Statistics Using R (FBDAPR) <https://www.prstats.org/course/introduction-fundamentals-of-bayesian-data-analysis-statistics-using-r-fbdapr/> Bayesian Data Analysis (BADAPR) <https://www.prstats.org/course/bayesian-data-analysis-badapr/> Bayesian Approaches To Regression And Mixed Effects Models Using R And brms (BARMPR) <https://www.prstats.org/course/bayesian-approaches-to-regression-and-mixed-effects-models-using-r-and-brms-barmpr/> Bayesian Hierarchical Modelling Using R (IBHMPR) <https://www.prstats.org/course/bayesian-hierarchical-modelling-using-r-ibhmpr/> Introduction To Stan For Bayesian Data Analysis (ISBDPR) <https://www.prstats.org/course/introduction-to-stan-for-bayesian-data-analysis-isbdpr/> Please email oliverhoo...@prstatistics.com with any questions. Oliver -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Tidyverse for Ecologists (TIDY01)
ONLINE COURSE – Tidyverse for Ecologists (TIDY01) https://www.prstats.org/course/tidyverse-for-ecologists-tidy01/ 16th - 20th June Please feel free to share! COURSE OVERVIEW - This course comprehensively introduces the Tidyverse and focuses on its use in data science projects. It is designed to give participants a strong foundation in R programming, core Tidyverse packages, and the Tidymodels framework. The course emphasises hands-on projects to apply learned concepts to real-world data analysis and modelling tasks applied to biology. By the end of the course, participants should: Understand the fundamentals of R programming for data analysis. Be proficient in using core Tidyverse packages to clean, transform, and visualise data. Gain an introduction to basic machine learning concepts through the Tidymodels framework. Learn to preprocess, build, evaluate, and interpret models using Tidymodels. Apply Tidyverse and Tidymodels tools to solve real-world problems through hands-on projects. Please email oliverhoo...@prstatistics.com with any questions Day 1: A Short Course in R Basics (9:30 - 17:30) This day provides participants with the foundational R skills required for working with Tidyverse and Tidymodels. It is designed for beginners or those needing a refresher in R programming. Section 1 (R Essentials): This section focuses on R syntax, variables, data types, conditionals (`if`, `else`, `elif`), loops (`for`, `while`), and writing reusable code using functions. Section 2 (Data Structures and File Handling in R): This section emphasises understanding data structures (e.g., vectors, data frames, lists) and handling files by reading/writing data (e.g., CSVs) for manipulation and analysis. Day 2: Fundamentals of Tidyverse I (9:30 - 17:30) This day introduces participants to the foundational concepts of Tidyverse packages and their applications to data science projects. Section 3 (Data Manipulation I): This section covers the basics of data manipulation using `dplyr` functions such as `filter()`, `select()`, `mutate()`, `arrange()`, and `summarise ()`. Participants will learn how to clean, transform, and prepare datasets for analysis. Section 4 (Data Visualisation I): This section introduces the principles of data visualisation using `ggplot2`. Participants will learn how to create basic plots such as scatterplots, bar charts, and line graphs while exploring the grammar of graphics. Day 3: Fundamentals of Tidyverse II (9:30 - 17:30) This day builds on the foundations established in Day 2 and dives deeper into advanced data manipulation and visualisation techniques. Section 5 (Data Manipulation II): This section extends the use of `dplyr` by introducing more complex operations such as joins, grouping with `group_by()`, and working with pipelines using `%>%`. Finally, additional packages will be presented to enhance data manipulation programming. Section 6 (Data Visualisation II): Participants will explore advanced visualisation techniques using extensions of `ggplot2`, such as creating animated plots with the `gganimate` package and interactive visualisations with additional tools. Day 4: Applying Tidyverse Fundamentals to Data Modelling (9:30 - 17:30) This day introduces participants to machine learning concepts using core libraries for statistical modelling and deep learning. Section 7 (Introduction to regression): This section focuses on regression modelling using Tidymodels. Participants will learn to implement linear regression models, evaluate model performance, and interpret results. Section 8 (Introduction to Classification): This section introduces techniques such as support vector machines and neural networks using Tidymodels. Participants will also explore methods for assessing the performance of classification models. Day 5: Data Science Workflow with Tidyverse (9:30 - 17:30) On the final day, participants will apply all their newly acquired skills to solve real-world problems inspired by ecological datasets. Section 9 (The data science workflow): The workflow will be illustrated based on the core packages introduced. The book "R for Data Science" will serve as a base literature for this day Section 10 (Hands-on project): Participants will work through a complete data science workflow, including data cleaning, transformation, visualisation, modelling, and communication of results. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Machine Learning using Python (MLUP01)
Machine Learning using Python (MLUP01) https://www.prstats.org/course/machine-learning-using-python-mlup01/ February 2nd - 13th June 2025 (10 x 1/2 days) Please feel free to share! *ABOUT THIS COURSE.* Machine vision has produced many helpful image-processing techniques in several fields, such as object detection, classification, and segmentation. Machine vision is an interdisciplinary discipline combining computer vision and machine learning methods, mainly deep learning, to solve vision problems. Common problems, such as classification and localisation, are typical examples that combine these research fields. These techniques have applications in many areas. Deep learning methods are commonly applied for image classification, focusing on deep neural networks and Convolutional Neural Networks (CNNs), including concepts of transfer learning applied to image classification. This course introduces basic concepts of deep learning and machine vision applied to image classification using CNNs. To illustrate these methods, a dataset of medically and forensically important flies is used. Other examples will also be used during the course to illustrate the applications of machine vision in ecology. By the end of the course, participants should: - Understand the basic concepts behind the machine vision ecosystem in Python; - Understand the machine vision pipeline workflow; - Understand the application of standard Python packages such as OpenCV and Tensorflow; - Understand the basic concepts behind Deep Neural Networks; - Understand the basic concepts behind Convolutional Deep Neural Networks; - Understand basic concepts behind Transfer learning; - Have the confidence to implement basic Machine vision methods using Python; - Have the confidence to combine basic computer vision and machine learning methods to perform vision tasks; Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE - Movement Ecology Using R (MOVE06)
ONLINE COURSE - Movement Ecology Using R (MOVE06) https://www.prstats.org/course/online-course-movement-ecology-move06/ Delivered by Prof. Luca Borga and Prof. Garrett Street 13th - 17th May 2024 Please feel free to share! This is a fully live course with two instrcutors alwys availble during lectures and practicals so you will alwys have support on hand. We alsorecord all live sessions to accommodate different time zones. All attendees will have access to recordings for a further 3 months after the course to revisit any of the classes. COURSE OVERVIEW - The course will cover the concepts, technology and software tools that can be used to analyse movement data (from ringing/CMR to VHF/GPS) in ecology and evolution. We will cover elementary and advanced analysis and modelling techniques broadly applicable across taxa, from micro-organisms to vertebrates, highlighting the advantages of a unified Movement Ecology framework. We will provide the necessary bases in ecology (especially behavioural ecology), physics and mathematics/statistics, to be able to identify for any specific research question the most appropriate study species, logging technology (incl. attachment methods), and statistical/mathematical modelling approach. We will specifically address the challenges and opportunities at each of the steps of the proposed ‘question-driven approach’, combining theory with computer-based practicals in R. We will also address the challenges of applying the results of the analyses to applied management problems and communicate the findings to non-experts. Please email oliverhoo...@prstatistics.com with any questions Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE - Movement Ecology Using R (MOVE07)
ONLINE COURSE - Movement Ecology Using R (MOVE07) *Please ignore the last post (incorrect dates and link)* https://www.prstats.org/course/online-course-movement-ecology-move07/ <https://www.prstats.org/course/online-course-movement-ecology-move06/> Delivered by Prof. Luca Borga and Prof. Garrett Street 12th - 16th May 2025 Please feel free to share! This is a fully live course with two instrcutors alwys availble during lectures and practicals so you will alwys have support on hand. We alsorecord all live sessions to accommodate different time zones. All attendees will have access to recordings for a further 3 months after the course to revisit any of the classes. COURSE OVERVIEW - The course will cover the concepts, technology and software tools that can be used to analyse movement data (from ringing/CMR to VHF/GPS) in ecology and evolution. We will cover elementary and advanced analysis and modelling techniques broadly applicable across taxa, from micro-organisms to vertebrates, highlighting the advantages of a unified Movement Ecology framework. We will provide the necessary bases in ecology (especially behavioural ecology), physics and mathematics/statistics, to be able to identify for any specific research question the most appropriate study species, logging technology (incl. attachment methods), and statistical/mathematical modelling approach. We will specifically address the challenges and opportunities at each of the steps of the proposed ‘question-driven approach’, combining theory with computer-based practicals in R. We will also address the challenges of applying the results of the analyses to applied management problems and communicate the findings to non-experts. Please email oliverhoo...@prstatistics.com with any questions Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11)
*ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11)* https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/ 25th - 28th March 2025 Instructor - Dr. Andrew Jackson COURSE OVERVIEW: This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. Recently SIMMs have become a very popular tool for quantifying food webs and thus the diet of predators and prey in an ecosystem. Starting with only basic understanding of statistical models, we will cover the do’s and don’ts of using SIMMs with a particular focus on the widely used package SIAR and the more advanced MixSIAR. Participants will be taught some of the advanced features of these packages, which will enable them to produce a richer class of output, and are encouraged to bring their own data sets and problems to study during the round-table discussions. DAY 1 Basic concepts. Module 1: Introduction; why use a SIMM? Module 2: An introduction to Bayesian statistics. Module 3: Differences between regression models and SIMMs. Practical: Revision on using R to load data, create plots and fit statistical models. Round table discussion: Understanding the output from a Bayesian model. DAY 2 Understanding and using SIAR. Module 4: Do’s and Don’ts of using SIAR. Module 5: The statistical model behind SIAR. Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots. Round table discussion: Issues when using simple SIMMs. DAY 3 SIBER and MixSIAR. Module 6: Creating and understanding Stable Isotope Bayesian Ellipses (SIBER). Module 7: What are the differences between SIAR and MixSIAR? Practical: Using MixSIAR on real world data sets; benefits over SIAR. Round table discussion: When to use which type of SIMM. DAY 4 Advanced SIMMs. Module 8: Using MixSIAR for complex data sets: time series and mixed effects models. Module 9: Source grouping: when and how? Module 10: Building your own SIMM with JAGS. Practical: Running advanced SIMMs with JAGS. Round table discussion: Bring your own data set. Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE - Visual Exploration, Analysis, and Presentation of Spatial Data using the ‘tmap’ Package (TMAP01)
ONLINE COURSE - Visual Exploration, Analysis, and Presentation of Spatial Data using the ‘tmap’ Package (TMAP01) <https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/> https://www.prstats.org/course/visual-exploration-analysis-and-presentation-of-spatial-data-using-the-tmap-package-tmap01/ 6th - 9th May 2025 Instructor - Dr Martijn Tennekes COURSE OVERVIEW: R statistical software is becoming increasingly popular for spatial analysis and visualization—and for good reason. It is reproducible, flexible, and supported by a vast ecosystem of R packages dedicated to spatial data. An essential part of working with spatial data is visualization, not only for communication but also for exploration and analysis. This in-depth course focuses on the R package *tmap*, one of the most widely used packages for spatial data visualization. The course covers all key steps, from reading spatial data to publishing high-resolution static maps or interactive maps that can be embedded in web articles and dashboards. Participants will work with essential spatial data packages in particular *sf*, *terra*, and *stars*. The course also addresses key methodological aspects of spatial data visualization, including map projections, selecting the most appropriate visualization method for a given task, and choosing color schemes that account for accessibility and cultural considerations. Innovative spatial visualization techniques are also explored, including cartograms, grid maps (also known as origin-destination maps), and glyph-based visualizations. By the end of the course, participants will: - Know how to use the core packages *sf*, *terra*, and *stars* to read and process spatial data in R, including joining data sources and performing geospatial data manipulations. - Be able to use *tmap* for exploring, analysing, and presenting spatial data. - Create various types of thematic maps. - Understand the methodological advantages and limitations of different map types, enabling informed decisions based on data characteristics and target users. - Recognize key considerations when selecting a suitable colour palette. - Be able to fine-tune map layouts in *tmap*, including adding map components, customizing legends, and incorporating map insets. - Know how to export maps in various static and interactive formats. DAY 1 - *Classes from 12:00 – 20:00 UK local time* *Getting started * - Overview of the core R packages for spatial data analysis and visualisation. - Overview of resources: online books, Stack Overflow, GitHub, etc. - Methodology of spatial data visualisation. - Creating common thematic map types using the R package *tmap*. - Exploring colour palettes with the R package *cols4all*. *DAY 2 - **Classes from 12:00 – 20:00 **UK local time* *Visualisation of spatial vector data in R* - Introduction to map projections (coordinate reference systems). - Working with vector data in R using the *sf* package. - Reading and writing spatial vector data in various formats. - Joining spatial and non-spatial data. - Geospatial data manipulations. - Visualisation of vector data with *tmap*. - Using basemaps in *tmap*. - Creating cartograms with the R package *tmap.cartogram*. *DAY 3 - **Classes from 12:00 – 20:00 **UK local time* *Visualisation of spatial raster data in R* - Working with raster data in R using the *terra* package. - Working with spatiotemporal data cubes in R using the *stars* package. - Reading and writing spatial raster data in various formats. - Downsampling, warping, and transforming raster data. - Converting spatial vector data to raster data and vice versa. - Visualisation of raster data with *tmap*. *DAY 4 - **Classes from 12:00 – 20:00 **UK local time* *Finalising and exporting maps* - Exporting static maps to various formats, including bitmap (JPG, PNG) and vector formats (SVG, PDF). - Exporting interactive maps to HTML. - Integrating *tmap* with the R package *shiny* for dashboards. - Fine-tuning map layouts for high-quality publications. - Extensibility of *tmap* Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07)
ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/ <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr06/> *31st - March - 4th April 2025* Please feel free to share! We encourage attendees to bring their own data, you will receive opportunities to discuss your data with the instructor throughout the course, if you would like guideline on how to organize your data prior to the course please ask oliverhooo...@prstatistica.com This course is suitable for researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches, with applications to biogeography, spatial ecology, biodiversity conservation and related disciplines. This 5-day course will cover R concepts, methods, and tools that can be used to analyze community ecology data. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity indices, distance measures and distance-based multivariate methods, clustering, classification and ordination techniques using the R package VEGAN. We will use real-world empirical data sets to motivate analyses, such as describing patterns along gradients of environ-mental or anthropogenic disturbances, and quantifying the effects of continuous and discrete predictors. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures, guided computer coding, and participant exercises. The course is intended for intermediate users of R who are interested in community ecology, particularly in the areas of terrestrial and wetland ecology, microbial ecology, and natural resource management. You are strongly encouraged to use your own data sets (they should be clean and already structured, see the document: “recommendation if you participate with your data”. *Classes will run from 08:00 – 13:00 for the morning lecture and 14:00 – 16:00 for the practical (UK time) with an evening time session tbc for US, Canada etc. attendees. The course will be recorded and made available each day and will remain available for 28 days after the course for you to revisit any lectures.* DAY 1 • Module 1: Introduction to community data analysis, basics of programming in R • Module 2: Diversity analysis, species-abundance distributions DAY 2 • Module 3: Distance and transformation measures • Module 4: Clustering and classification analysis DAY 3 • Module 5: Unconstrained ordinations: Principal Component Analysis • Module 6: Other unconstrained ordinations DAY 4 • Module 7: Constrained ordinations: RDA and other canonical analysis • Module 8: Statistical tests for multivariate data and variation partitioning DAY 5 • Module 9: Overview of Spatial analysis, and recent Hierarchical Modeling of Species Communities (HMSC) methods • Modules 10: Special topics and discussion, analyzing participants’ data. Email oliverghoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE - Movement Ecology Using R (MOVE07) Prof. Luca Borger and Prof. Garret Street
ONLINE COURSE - Movement Ecology Using R (MOVE07) https://www.prstats.org/course/online-course-movement-ecology-move07/ <https://www.prstats.org/course/online-course-movement-ecology-move06/> Delivered by Prof. Luca Borga and Prof. Garrett Street 12th - 16th May 2025 Please feel free to share! This is a fully live course with two instructors always available during lectures and practicals so you will always have support on hand. We also record all live sessions to accommodate different time zones. All attendees will have access to recordings for a further 3 months after the course to revisit any of the classes. COURSE OVERVIEW - The course will cover the concepts, technology and software tools that can be used to analyse movement data (from ringing/CMR to VHF/GPS) in ecology and evolution. We will cover elementary and advanced analysis and modelling techniques broadly applicable across taxa, from micro-organisms to vertebrates, highlighting the advantages of a unified Movement Ecology framework. We will provide the necessary bases in ecology (especially behavioural ecology), physics and mathematics/statistics, to be able to identify for any specific research question the most appropriate study species, logging technology (incl. attachment methods), and statistical/mathematical modelling approach. We will specifically address the challenges and opportunities at each of the steps of the proposed ‘question-driven approach’, combining theory with computer-based practicals in R. We will also address the challenges of applying the results of the analyses to applied management problems and communicate the findings to non-experts. Please email oliverhoo...@prstatistics.com with any questions -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11)
*ONLINE COURSE – Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM11)* https://www.prstats.org/course/online-course-stable-isotope-mixing-models-using-siber-siar-mixsiar-simm11/ 25th - 28th March 2025 Instructor - Dr. Andrew Jackson COURSE OVERVIEW: This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. Recently SIMMs have become a very popular tool for quantifying food webs and thus the diet of predators and prey in an ecosystem. Starting with only basic understanding of statistical models, we will cover the do’s and don’ts of using SIMMs with a particular focus on the widely used package SIAR and the more advanced MixSIAR. Participants will be taught some of the advanced features of these packages, which will enable them to produce a richer class of output, and are encouraged to bring their own data sets and problems to study during the round-table discussions. DAY 1 Basic concepts. Module 1: Introduction; why use a SIMM? Module 2: An introduction to Bayesian statistics. Module 3: Differences between regression models and SIMMs. Practical: Revision on using R to load data, create plots and fit statistical models. Round table discussion: Understanding the output from a Bayesian model. DAY 2 Understanding and using SIAR. Module 4: Do’s and Don’ts of using SIAR. Module 5: The statistical model behind SIAR. Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots. Round table discussion: Issues when using simple SIMMs. DAY 3 SIBER and MixSIAR. Module 6: Creating and understanding Stable Isotope Bayesian Ellipses (SIBER). Module 7: What are the differences between SIAR and MixSIAR? Practical: Using MixSIAR on real world data sets; benefits over SIAR. Round table discussion: When to use which type of SIMM. DAY 4 Advanced SIMMs. Module 8: Using MixSIAR for complex data sets: time series and mixed effects models. Module 9: Source grouping: when and how? Module 10: Building your own SIMM with JAGS. Practical: Running advanced SIMMs with JAGS. Round table discussion: Bring your own data set. Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Visual Exploration, Analysis, and Presentation of Spatial Data using the ‘tmap’ Package (TMAP01) Martijn Tennekes
ONLINE COURSE – Visual Exploration, Analysis, and Presentation of Spatial Data using the ‘tmap’ Package (TMAP01) https://www.prstats.org/course/visual-exploration-analysis-and-presentation-of-spatial-data-using-the-tmap-package-tmap01/ Instructor - Dr. Martijn Tennekes (author of the tmap package) 6th - 9th May 2025 Please feel free to share! ABOUT THIS COURSE - R statistical software is becoming increasingly popular for spatial analysis and visualization—and for good reason. It is reproducible, flexible, and supported by a vast ecosystem of R packages dedicated to spatial data. An essential part of working with spatial data is visualization, not only for communication but also for exploration and analysis. This in-depth course focuses on the R package *tmap*, one of the most widely used packages for spatial data visualization. The course covers all key steps, from reading spatial data to publishing high-resolution static maps or interactive maps that can be embedded in web articles and dashboards. Participants will work with essential spatial data packages in particular *sf*, *terra*, and *stars*. The course also addresses key methodological aspects of spatial data visualization, including map projections, selecting the most appropriate visualization method for a given task, and choosing color schemes that account for accessibility and cultural considerations. Innovative spatial visualization techniques are also explored, including cartograms, grid maps (also known as origin-destination maps), and glyph-based visualizations. By the end of the course, participants will: - Know how to use the core packages *sf*, *terra*, and *stars* to read and process spatial data in R, including joining data sources and performing geospatial data manipulations. - Be able to use *tmap* for exploring, analysing, and presenting spatial data. - Create various types of thematic maps. - Understand the methodological advantages and limitations of different map types, enabling informed decisions based on data characteristics and target users. - Recognize key considerations when selecting a suitable colour palette. - Be able to fine-tune map layouts in *tmap*, including adding map components, customizing legends, and incorporating map insets. - Know how to export maps in various static and interactive formats. Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01)
ONLINE COURSE – Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live https://www.prstats.org/course/phylogenetic-species-distribution-modelling-using-r-psdm01-25/ 12th - 14th May 2025 Instructor - Dr. Morales Castilla Ignacio Please feel free to share! COURSE OVERVIEW: In this three-day course, we introduce species distribution models (SDMs) and ways to incorporate phylogenetic information into single species models using R. We begin by providing an overview on the use of SDMs as a central tool for ecologists and evolutionary biologists, review and implement common SDM approaches and introduce hybrid models, which use the information in functional traits to complement the models. We then justify the rationale for using phylogenetic information in absence of functional trait data and show how to incorporate phylogenetic information in SDMs (day 1). We review examples of practical implementation of PSDMs to both present and future climate scenarios (day 2). Finally, we overview more advanced approaches of incorporating phylogenies into models (the Bayesian Phylogenetic Mixed Model) and how to project model results into a spatial context (day 3). Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L 2 places left - Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07)
ONLINE COURSE – Multivariate Analysis Of Ecological Communities Using R With The VEGAN package (VGNR07) Only 2 places left! https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr07/ <https://www.prstats.org/course/multivariate-analysis-of-ecological-communities-using-r-with-the-vegan-package-vgnr06/> *31st - March - 4th April 2025* Please feel free to share! We encourage attendees to bring their own data, you will receive opportunities to discuss your data with the instructor throughout the course, if you would like guideline on how to organize your data prior to the course please ask oliverhooo...@prstatistica.com This course is suitable for researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches, with applications to biogeography, spatial ecology, biodiversity conservation and related disciplines. This 5-day course will cover R concepts, methods, and tools that can be used to analyze community ecology data. The course will review data processing techniques relevant to multivariate data sets. We will cover diversity indices, distance measures and distance-based multivariate methods, clustering, classification and ordination techniques using the R package VEGAN. We will use real-world empirical data sets to motivate analyses, such as describing patterns along gradients of environ-mental or anthropogenic disturbances, and quantifying the effects of continuous and discrete predictors. We will emphasise visualisation and reproducible workflows as well as good programming practices. The modules will consist of introductory lectures, guided computer coding, and participant exercises. The course is intended for intermediate users of R who are interested in community ecology, particularly in the areas of terrestrial and wetland ecology, microbial ecology, and natural resource management. You are strongly encouraged to use your own data sets (they should be clean and already structured, see the document: “recommendation if you participate with your data”. *Classes will run from 08:00 – 13:00 for the morning lecture and 14:00 – 16:00 for the practical (UK time) with an evening time session tbc for US, Canada etc. attendees. The course will be recorded and made available each day and will remain available for 28 days after the course for you to revisit any lectures.* DAY 1 • Module 1: Introduction to community data analysis, basics of programming in R • Module 2: Diversity analysis, species-abundance distributions DAY 2 • Module 3: Distance and transformation measures • Module 4: Clustering and classification analysis DAY 3 • Module 5: Unconstrained ordinations: Principal Component Analysis • Module 6: Other unconstrained ordinations DAY 4 • Module 7: Constrained ordinations: RDA and other canonical analysis • Module 8: Statistical tests for multivariate data and variation partitioning DAY 5 • Module 9: Overview of Spatial analysis, and recent Hierarchical Modeling of Species Communities (HMSC) methods • Modules 10: Special topics and discussion, analyzing participants’ data. Email oliverghoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Introduction to generalised linear models using R and Rstudio (IGLM08)
ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/ 21st - 23rd May 2025 This course provides a comprehensive practical and theoretical introduction to generalized linear models using R. Generalized linear models are generalizations of linear regression models for situations where the outcome variable is, for example, a binary, or ordinal, or count variable, etc. The specific models we cover include binary, binomial, and categorical logistic regression, Poisson and negative binomial regression for count variables, as well as extensions for overdispersed and zero-inflated data. We begin by providing a brief overview of the normal general linear model. Understanding this model is vital for the proper understanding of how it is generalized in generalized linear models. Next, we introduce the widely used binary logistic regression model, which is is a regression model for when the outcome variable is binary. Next, we cover the binomial logistic regression, and the multinomial case, which is for modelling outcomes variables that are polychotomous, i.e., have more than two categorically distinct values. We will then cover Poisson regression, which is widely used for modelling outcome variables that are counts (i.e the number of times something has happened). We then cover extensions to accommodate overdispersion, starting with the quasi-likelihood approach, then covering the negative binomial and beta-binomial models for counts and discrete proportions, respectively. Finally, we will cover zero-inflated Poisson and negative binomial models, which are for count data with excessive numbers of zero observations. *Day 1* Topic 1: The general linear model. We begin by providing an overview of the normal, as in normal distribution, general linear model, including using categorical predictor variables. Although this model is not the focus of the course, it is the foundation on which generalized linear models are based and so must be understood to understand generalized linear models. Topic 2: Binary logistic regression. Our first generalized linear model is the binary logistic regression model, for use when modelling binary outcome data. We will present the assumed theoretical model behind logistic regression, implement it using R’s glm, and then show how to interpret its results, perform predictions, and (nested) model comparisons. Topic 3: Binomial logistic regression. Here, we show how the binary logistic regresion can be extended to deal with data on discrete proportions. We will also present alternative link functions to the logit, such as the probit and complementary log-log links. *Day 2* Topic 4: Categorical logistic regression. Categorical logistic regression, also known as multinomial logistic regression, is for modelling polychotomous data, i.e. data taking more than two categorically distinct values. Like ordinal logistic regression, categorical logistic regression is also based on an extension of the binary logistic regression case. Topic 5: Poisson regression. Poisson regression is a widely used technique for modelling count data, i.e., data where the variable denotes the number of times an event has occurred. *Day 3* Topic 6: Overdispersion models. The quasi-likelihood approach for both the Poisson and binomial models. Negative binomial regression. The negative binomial model is, like the Poisson regression model, used for unbounded count data, but it is less restrictive than Poisson regression, specifically by dealing with overdispersed data. Beta-binomial regression. The beta-binomial model is an overdispersed alternative to the binomial. Topic 7: Zero inflated models. Zero inflated count data is where there are excessive numbers of zero counts that can be modelled using either a Poisson or negative binomial model. Zero inflated Poisson or negative binomial models are types of latent variable models. Please email oliverhoo...@prstatistics.com with any questions. Please feel free to share among colleagues and friends -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
Ecolog-L Learn how to use R for the most commonly encountered statistics at your own pace.
Do you want to learn how to use R for the most commonly encountered statistics but at your own pace? If so, check out on demand courses! You have access for 28 days and instructor support via email. Please feel free to share! FREE Introduction To Statistics Using R And Rstudio (IRRSPR) <https://www.prstats.org/course/introduction-to-statistics-using-r-and-rstudio-irrs03r/> Advancing in R (ADVRPR) <https://www.prstats.org/course/advancing-in-r-advrpr/> Introduction To Generalised Linear Models Using R And Rstudio (IGLMPR) <https://www.prstats.org/course/online-course-introduction-to-generalised-linear-models-using-r-and-rstudio-iglmpr/> Introduction To Mixed Models Using R And Rstudio (IMMRPR) <https://www.prstats.org/course/online-course-introduction-to-mixed-models-using-r-and-rstudio-immrpr/> Model Selection And Model Simplification (MSMSPR) <https://www.prstats.org/course/online-course-model-selection-and-model-simplification-msmspr/> -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
