Joint Mean-Covariance Models using ‘Armadillo’ and S4 (jmcm)
Fit joint mean-covariance models for longitudinal data. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the ‘Armadillo’ C++ library for numerical linear algebra and ‘RcppArmadillo’ glue.
Hidden Markov Models for Life Sequences and Other Multivariate, Multichannel Categorical Time Series (seqHMM)
Designed for fitting hidden (latent) Markov models and mixture hidden Markov models for social sequence data and other categorical time series. Also some more restricted versions of these type of models are available: Markov models, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models. The package provides functions for evaluating and comparing models, as well as functions for easy plotting of multichannel sequence data and hidden Markov models. Models are estimated using maximum likelihood via the EM algorithm and/or direct numerical maximization with analytical gradients. All main algorithms are written in C++ with support for parallel computation.
Sensitivity Analysis for 2x2xk Tables in Observational Studies (sensitivity2x2xk)
Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.