R functions for the estimation and eigen-decomposition of multivariate autoregressive models.

Multiplicative AR(1) with Seasonal is a stochastic
  process model built on top of AR(1). The package provides the
  following procedures for MAR(1)S processes: fit, compose, decompose,
  advanced simulate and predict.

Supports visual interpretation of hierarchical composite
    endpoints (HCEs). HCEs are complex constructs used as primary endpoints in
    clinical trials, combining outcomes of different types into ordinal endpoints,
    in which each patient contributes the most clinically important event (one and
    only one) to the analysis. See Karpefors M et al. (2022)
    <doi:10.1177/17407745221134949>.

Recently, multiple marginal variable selection methods have been developed and shown to be effective in Gene-Environment interactions studies. We propose a novel marginal Bayesian variable selection method for Gene-Environment interactions studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo. The core algorithms of the package have been developed in 'C++'.

Computation of various Markovian models for categorical data
    including homogeneous Markov chains of any order, MTD models, Hidden Markov
    models, and Double Chain Markov Models.

A set of tools for likelihood-based estimation, model selection and testing of two- and three-range shift and migration models for animal movement data as described in Gurarie et al. (2017) <doi: 10.1111/1365-2656.12674>.  Provided movement data (X, Y and Time), including irregularly sampled data, functions estimate the time, duration and location of one or two range shifts, as well as the ranging area and auto-correlation structure of the movment.  Tests assess, for example, whether the shift was "significant", and whether a two-shift migration was a true return migration.