Performs bivariate composite likelihood and full
        information maximum likelihood estimation for polytomous
        logit-normit (graded logistic) item response theory (IRT)
        models.

The Poisson-lognormal model and variants (Chiquet,
    Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be
    used for a variety of multivariate problems when count data are at
    play, including principal component analysis for count data,
    discriminant analysis, model-based clustering and network inference.
    Implements variational algorithms to fit such models accompanied with
    a set of functions for visualization and diagnostic.

A comprehensive framework for planning and executing analyses in R. 
    It provides a structured approach to running the same function multiple times 
    with different arguments, executing multiple functions on the same datasets, 
    and creating systematic analyses across multiple strata or variables. 
    The framework is particularly useful for applying the same analysis across 
    multiple strata (e.g., locations, age groups), running statistical methods 
    on multiple variables (e.g., exposures, outcomes), generating multiple tables 
    or graphs for reports, and creating systematic surveillance analyses. 
    Key features include efficient data management, structured analysis planning, 
    flexible execution options, built-in debugging tools, and hash-based caching.

A method for the quantitative prediction with much predictors. 
    This package provides functions to construct the quantitative prediction 
    model with less overfitting and robust to noise.

Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.

Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, etc. Includes data sets from oceanography.

The 'rgl' implementation of plot3D functions.

An implementation of the ternary plot for interpreting regression
    coefficients of trinomial regression models, as proposed in Santi, Dickson
    and Espa (2019) <doi:10.1080/00031305.2018.1442368>. Ternary plots can be
    drawn using either 'ggtern' package (based on 'ggplot2') or 'Ternary'
    package (based on standard graphics). The package and its features are
    illustrated in Santi, Dickson, Espa and Giuliani (2022)
    <doi:10.18637/jss.v103.c01>.