Computes efficient data distributions from highly inconsistent datasets with many missing values using multi-set intersections. Based upon hash functions, 'mulset' can quickly identify intersections from very large matrices of input vectors across columns and rows and thus provides scalable solution for dealing with missing values. Tomic et al. (2019) <doi:10.1101/545186>.

Fitting multivariate response models with random effects on one or two levels; whereby the (one-dimensional) random effect represents a latent variable approximating the multivariate space of outcomes, after possible adjustment for covariates. The method is particularly useful for multivariate, highly correlated outcome variables with unobserved heterogeneities. Applications include regression with multivariate responses, as well as multivariate clustering or ranking problems. See Zhang and Einbeck (2024) <doi:10.1007/s42519-023-00357-0>.

Integer Programming Formulations Applied to Univariate and Multivariate Allocation Problems.

Several multivariate techniques from a biplot perspective. It is the translation (with many improvements) into R of the previous package developed in 'Matlab'. The package contains some of the main developments of my team during the last 30 years together with some more standard techniques. Package includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots, Correspondence Analysis, Canonical Correspondence Analysis, Canonical STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional Unfolding, External Biplots for Principal Coordinates Analysis or Multidimensional Scaling, among many others. References can be found in the help of each procedure.

Contains auxiliary routines for influx software. This packages is not intended to be used directly. Influx was published here: Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.

Simultaneous tests and confidence intervals
  for general linear hypotheses in parametric models, including 
  linear, generalized linear, linear mixed effects, and survival models.
  The package includes demos reproducing analyzes presented
  in the book "Multiple Comparisons Using R" (Bretz, Hothorn, 
  Westfall, 2010, CRC Press).