Functions for genome-wide association studies (GWAS)/gene-environment-wide interaction studies (GEWIS) with longitudinal outcomes and exposures. He et al. (2017) "Set-Based Tests for Gene-Environment Interaction in Longitudinal Studies" and He et al. (2017) "Rare-variant association tests in longitudinal studies, with an application to the Multi-Ethnic Study of Atherosclerosis (MESA)".

For high-dimensional correlated observations, this package carries out the L_1 penalized maximum likelihood 
            estimation of the precision matrix (network) and the correlation parameters. The correlated data can be 
            longitudinal data (may be irregularly spaced) with dampening correlation or clustered data with uniform correlation.  
            For the details of the algorithms, please see the paper Jie Zhou et al. Identifying Microbial Interaction Networks Based on Irregularly Spaced 
            Longitudinal 16S rRNA sequence data <doi:10.1101/2021.11.26.470159>.

Interpretable nonparametric modeling of longitudinal data
    using additive Gaussian process regression. Contains functionality
    for inferring covariate effects and assessing covariate relevances.
    Models are specified using a convenient formula syntax, and can include
    shared, group-specific, non-stationary, heterogeneous and temporally
    uncertain effects. Bayesian inference for model parameters is performed
    using 'Stan'. The modeling approach and methods are described in detail in
    Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.

A flexible, light-weight logging
    framework based on 'R6' classes. It supports hierarchical loggers,
    structured logging, output to  plaintext, 'JSON', (rotating) files,
    and memory buffers. Additional appenders for 'Elasticsearch', 'Dynatrace', 
    databases, email and push notifications, and more are available
    in the 'lgrExtra' package.

A largish collection of example datasets, including several classics. Many of
    these datasets are well suited for regression, classification, and visualization.