A unified interface to fit, cross-validate and benchmark genomic
    prediction models from SNP marker data. It implements genomic best linear
    unbiased prediction (GBLUP) and ridge-regression BLUP in base R, and offers
    a common interface to machine-learning predictors (elastic net, random
    forest and gradient boosting) through optional packages, together with a
    stacked ensemble. Cross-validation uses breeding-relevant schemes and
    reports prediction accuracy honestly, so models can be compared fairly. The
    genomic relationship matrix follows VanRaden (2008)
    <doi:10.3168/jds.2007-0980>; the mixed-model solver follows Endelman (2011)
    <doi:10.3835/plantgenome2011.08.0024>; the genomic-selection framework
    follows Meuwissen, Hayes and Goddard (2001) <doi:10.1093/genetics/157.4.1819>.

Given an adjacency matrix drawn from a Generalized Stochastic Block Model with missing observations, this package robustly estimates the probabilities of connection between nodes and detects outliers nodes, as describes in Gaucher, Klopp and Robin (2019) <arXiv:1911.13122>.

Execute Latent Class Analysis (LCA) and Latent Class Regression (LCR) by using Generalized Structured Component Analysis (GSCA). This is explained in Ryoo, Park, and Kim (2019) <doi:10.1007/s41237-019-00084-6>.
    It estimates the parameters of latent class prevalence and item response probability in LCA with a single line comment. It also provides graphs of item response probabilities. In addition, the package enables to estimate the relationship between the prevalence and covariates.

Provides utilities for manipulating time series of location-based counts of events to detect geo-spatial clusters. Significance of these clusters is determined using a set of models that classify based on a learned relationship between observed and the log(observed/expected) ratio of counts. The approach implemented here is similar to prospective space-time estimation of clusters using the scan statistic.

Design and analysis of group sequential designs for negative
    binomial outcomes, as described by T Mütze, E Glimm, H Schmidli, T Friede (2018) <doi:10.1177/0962280218773115>.