GAMbler

Compositional data, where observations represent proportions that sum to a constant, are ubiquitous in ecology, yet many analysts fall back on problematic approaches like separate binomial models, beta regression on ratios, or worse, standard linear regre…

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I typically work in quantitative ecology and molecular epidemiology, where we use statistical models to predict species distributions or disease transmission patterns.

Vector Autoregressions (VAR models), also known in the ecological literature as Multivariate Autoregressions (MAR models), are a class of Dynamic Linear Models that offer a principled way to model both delayed and contemporaneous interactions among sets of

Seasonality is very common in real-world time series.

Welcoming mvgam to the Comprehensive R Archive Network (CRAN) The goal of mvgam is to use a Bayesian framework to estimate parameters of Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components.

What are Generalized Additive Models (GAMs)?

Purpose The following passage is quoted directly from Regression and other stories (Gelman, Hill and Vehtari 2022).

The goal of mvgam is to use a Bayesian framework to estimate parameters of Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components.

Required libraries cmdstanr Purpose and model introduction This script simulates binary observations of an imperfectly observed data generating process (i.e. our measurements are made with error).

I have been highly interested in multivariate time series modeling for the last few years and have spent a lot of time working out how to use hierarchical GAMs to tackle many of the questions I’m interested in.

Required libraries cmdstanr MASS raster ggplot2 viridis Purpose and model introduction This script simulates Poisson observations of an underlying spatially autocorrelated data generating process.

Here we will use mgcv to estimate parameters of nonlinear distributed lag models.

Generalized Additive Models (GAMs) are flexible tools that have found particular application in the analysis of time series data.

This goal of mrIML is to enable users to build and interpret multivariate machine learning models by harnessing the power and flexibility of the tidyverse (tidy model syntax in particular).

This goal of MRFcov is to approximate interaction parameters of nodes in undirected Markov Random Fields (MRF) graphical networks.

Hi, I'm the here-bot cat!

Pre-requisites First, make sure you have the latest version of the distill package installed from CRAN: install.packages("distill") Restart your R session.

Pre-requisites First, make sure you have the latest version of the postcards package installed from CRAN: install.packages("postcards") Restart your R session.

So far, we’ve been leveraging GitHub Pages for publishing.

Pre-requisites First, make sure you have the latest version of the blogdown package installed from CRAN: install.packages("blogdoown") Restart your R session.

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