Focusing on randomized paths between multiple locations (or populations, habitats, etc) Note This is simply a tutorial. I’m not (for now) providing a review of the literature surrounding ecological connectivity, or commenting the different meanings of connectivity).
Introduction to EDMs for Forecasting Non-stationary data EDMs are a data-driven solution for uncovering hidden dynamic behavior in natural systems, which are often complex and dynamic (referred to as “non-stationarity” or “non-linearity”).
Introduction linear regression with gradient descent This tutorial is a rough introduction into using gradient descent algorithms to estimate parameters (slope and intercept) for standard linear regressions, as an alternative to ordinary least squares (OLS) regression with a maximum likelihood estimator.
Introduction to machine learning with tidymodels Tidymodels provides a clean, organized, and–most importantly–consistent programming syntax for data pre-processing, model specification, model fitting, model evaluation, and prediction. Anatomy of tidymodels a meta-package that installs and load the core packages listed below that you need for modeling and machine learning rsamples: provides infrastructure for efficient data splitting and resampling parsnip: a tidy, unified interface to models that can be used to try a range of models without getting bogged down in the syntactical minutiae of the underlying packages recipes: a tidy interface to data pre-processing tools for feature engineering workflows: workflows bundle your pre-processing, modeling, and post-processing together tune: helps you optimize the hyperparameters of your model and pre-processing steps yardstick: measures the effectiveness of models using performance metrics dials: contains tools to create and manage values of tuning parameters and is designed to integrate well with the parsnip package broom: summarizes key information about models in tidy tibble()s First, lets load the tidymodels meta-package: