Metabarcoding Pipelines for Eukarriotic Communities (MPEC01) https://www.prstats.org/course/metabarcoding-pipelines-for-eukariotic-communities-mpec01/ Instructor-Dr. Adrià Antich 21st 25th June 2024 Please feel free to share! COURSE OVERVIEW - Metabarcoding has emerged as a pivotal technique, rapidly expanding and revolutionizing the way we study biodiversity. From soil samples to aquatic environments, metabarcoding provides insights into the diverse array of organisms present, offering crucial information for conservation efforts and ecological research. However, metabarcoding encounters intrinsic biases inherent in its methodology. Metabarcoding pipelines are designed to mitigate these biases, and this course will offer insights into optimizing these pipelines for accurate and reliable results. With new techniques continuously evolving, we'll explore methodologies geared towards unraveling both inter and intra-species diversity while addressing the common challenges encountered in a methodology. Additionally, we'll navigate the landscape of methods enabling comprehensive biodiversity assessments, alongside showcasing new machine learning approaches for inferring ecological quality status. This course will focus on the MJOLNIR3 pipeline and its theoretical framework. This R package is based on eight simple functions divided into four different blocks. For each function, a comprehensive description of the process will be provided, including alternatives from other pipelines and their basic command line usage. By the end of the course, participants will: Gain a comprehensive understanding of the theoretical foundations underpinning metabarcoding pipelines. Develop the ability to identify potential biases and effectively apply specialized software to mitigate them. Acquire proficiency in working across three distinct levels of coding requirements, encompassing command-line operations and graphical user interface packages. Demonstrate a thorough comprehension of basic biodiversity analysis techniques, spanning inter and intra-species levels. Please email oliverhooker@prstatistics.com with any questions. Oliver Hooker PhD. PR stats Oliver Hooker (to subscribe/unsubscribe the EvolDir send mail to golding@mcmaster.ca) ---- 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 17th - 26th October 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 oliverhooker@prstatistics.com with any questions. Oliver Hooker PhD. PR stats Oliver Hooker (to subscribe/unsubscribe the EvolDir send mail to golding@mcmaster.ca)