Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Computes factorial A-, D- and E-optimal designs for two-colour cDNA microarray experiments.

Uses raw vectors to minimize memory consumption of categorical 
    variables with fewer than 256 unique values. Useful for analysis of large
    datasets involving variables such as age, years, states, countries, or
    education levels.

Tests for Kaiser-Meyer-Olkin (KMO) and
    communalities in a dataset. It provides a final sample by removing
    variables in a iterable manner while keeping account of the variables
    that were removed in each step. It follows the best practices and assumptions according to
    Hair, Black, Babin & Anderson (2018, ISBN:9781473756540).

Estimation, model selection and goodness-of-fit of (1) factor copula models for mixed continuous and discrete data in Kadhem and Nikoloulopoulos (2021) <doi:10.1111/bmsp.12231>; (2) bi-factor and second-order copula models for item response data in Kadhem and Nikoloulopoulos (2023) <doi:10.1007/s11336-022-09894-2>; (3) factor tree copula models for item response data in Kadhem and Nikoloulopoulos (2022) <arXiv:2201.00339>.

Inference methods for factor copula models for continuous data in Krupskii and Joe (2013) <doi:10.1016/j.jmva.2013.05.001>, Krupskii and Joe (2015) <doi:10.1016/j.jmva.2014.11.002>, Fan and Joe (2024) <doi:10.1016/j.jmva.2023.105263>, one factor truncated vine models in Joe (2018) <doi:10.1002/cjs.11481>, and Gaussian oblique factor models. Functions for computing tail-weighted dependence measures in Lee, Joe and Krupskii (2018) <doi:10.1080/10485252.2017.1407414> and estimating tail dependence parameter.