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, feature-rich yet light-weight logging
    framework based on 'R6' classes. It supports hierarchical loggers,
    custom log levels, arbitrary data fields in log events, logging to
    plaintext, 'JSON', (rotating) files, memory buffers. For extra
    appenders that support logging to databases, email and push
    notifications see the the package lgr.app.

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

Additional appenders for the logging package 'lgr' that
    support logging to 'Elasticsearch', 'Dynatrace', 'AWSCloudWatchLog',
    databases, 'syslog', email- and push notifications, and more.

Functions for the longitudinal genetic random field method (He et al., 2015, <doi:10.1111/biom.12310>) to test the association between a longitudinally measured quantitative outcome and a set of genetic variants in a gene/region.

Implements Lagrangian multiplier smoothing splines for flexible
    nonparametric regression and function estimation. Provides tools for fitting,
    prediction, and inference using a constrained optimization approach to 
    enforce smoothness.
    Supports generalized linear models, Weibull accelerated failure time (AFT) models, 
    quadratic programming problems, and customizable arbitrary correlation structures. 
    Options for fitting in parallel are provided. The method builds upon the framework described 
    by Ezhov et al. (2018) <doi:10.1515/jag-2017-0029> using Lagrangian multipliers 
    to fit cubic splines. For more information on correlation structure estimation, 
    see Searle et al. (2009) <ISBN:978-0470009598>. For quadratic programming and 
    constrained optimization in general, see Nocedal & Wright (2006) <doi:10.1007/978-0-387-40065-5>. 
    For a comprehensive background on smoothing splines, see Wahba (1990) 
    <doi:10.1137/1.9781611970128> and Wood (2006) <ISBN:978-1584884743> 
    "Generalized Additive Models: An Introduction with R".