ICS via a Shiny Application (ICSShiny)
Performs Invariant Coordinate Selection (ICS) (Tyler, Critchley, Duembgen and Oja (2009) <doi:10.1111/j.1467-9868.2009.00706.x>) and especially ICS outlier identification (Archimbaud, Nordhausen, Ruiz-Gazen (2016) <arXiv:1612.06118>) using a shiny app.

Functional MRI Quality Assurance Routines (fmriqa)
Methods for performing fMRI quality assurance (QA) measurements of test objects. Heavily based on the fBIRN procedures detailed by Friedman and Glover (2006) <doi:10.1002/jmri.20583>.

Multivariate Tools for Compositional Data Analysis (ToolsForCoDa)
Provides functions for multivariate analysis with compositional data. Includes a function for doing compositional canonical correlation analysis. This analysis requires two data matrices of compositions, which can be adequately transformed and used as entries in a specialized program for canonical correlation analysis, that is able to deal with singular covariance matrices. The methodology is described in Graffelman et al. (2017) <doi:10.1101/144584>.

Tidy Characterizations of Model Performance (yardstick)
Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).

A-Optimal Block Designs for Comparing Test Treatments with Controls (Aoptbdtvc)
A collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics – Theory and Methods 46(8), 3728-3737.

Collection of Information-Based Feature Selection Filters (praznik)
A collection of feature selection filters performing greedy optimisation of mutual information-based usefulness criteria, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <http://…/brown12a.html>. Implements, among other, minimum redundancy maximal relevancy (‘mRMR’) method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information (‘JMI’) method by Yang and Moody (1999) <http://…-new-algorithms-for-nongaussian-data>; double input symmetrical relevance (‘DISR’) method by Meyer and Bontempi (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation (‘JMIM’) method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.