A comprehensive collection of linkage methods for agglomerative
  hierarchical clustering on a matrix of proximity data (distances or
  similarities), returning a multifurcated dendrogram or multidendrogram.
  Multidendrograms can group more than two clusters when ties in proximity data
  occur, and therefore they do not depend on the order of the input data.
  Descriptive measures to analyze the resulting dendrogram are additionally
  provided.

Functions for MultiDimensional Feature Selection (MDFS):
 calculating multidimensional information gains, scoring variables,
 finding important variables, plotting selection results.
 This package includes an optional CUDA implementation that speeds up
 information gain calculation using NVIDIA GPGPUs.
 R. Piliszek et al. (2019) <doi:10.32614/RJ-2019-019>.

'MTrackJ' is an 'ImageJ' plugin for motion tracking and analysis (see 
    <https://imagescience.org/meijering/software/mtrackj/>). This package reads 
    and writes 'MTrackJ Data Files' ('.mdf', see 
    <https://imagescience.org/meijering/software/mtrackj/format/>). It supports
    2D data and read/writes cluster, point, and channel information. If desired, 
    generates track identifiers that are unique over the clusters.
    See the project page for more information and examples.

Provides functions to impute missing values using Gaussian 
    copulas for mixed data types as described by Christoffersen et al. 
    (2021) <arXiv:2102.02642>. The method is related to Hoff (2007) 
    <doi:10.1214/07-AOAS107> and Zhao and Udell (2019) <arXiv:1910.12845> 
    but differs by making a direct approximation of the log marginal likelihood 
    using an extended version of the Fortran code created by Genz and Bretz 
    (2002) <doi:10.1198/106186002394> in addition to also support multinomial 
    variables.

Allows various models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance can be further modeled as random-effect models.