Mixture and flexible discriminant analysis, multivariate
        adaptive regression splines (MARS), BRUTO, and vector-response smoothing splines.
	Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York.

Multi-Dimensional Analysis (MDA) is an adaptation of factor
  analysis developed by Douglas Biber (1992) <doi:10.1007/BF00136979>. Its most
  common use is to describe language as it varies by genre, register, and use.
  This package contains functions for carrying out the calculations needed to
  describe and plot MDA results: dimension scores, dimension means, and factor
  loadings.

Learning a mixed directed acyclic graph based on both continuous and categorical data.

Projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics. S. Kucheryavskiy (2020) <doi:10.1016/j.chemolab.2020.103937>.

Provides a disciplined, lazy subset of 'dplyr' semantics for
    MongoDB aggregation pipelines. Queries remain lazy until collect() and
    compile into MongoDB-native aggregation stages.

Work with Microsoft Access '.mdb' and '.accdb' files using the
    open source 'MDB Tools' library <https://github.com/mdbtools/mdbtools/>.
    The library is compiled and bundled with the package, so no external
    installation is required. Provides high-level helpers for reading tables,
    exporting to CSV or JSON, inspecting table definitions, and running SQL queries.
    Also exposes a full read-only 'DBI' interface for use with standard
    database workflows.

Computes martingale difference correlation (MDC), martingale difference divergence, and their partial extensions to assess conditional mean dependence. The methods are based on Shao and Zhang (2014) <doi:10.1080/01621459.2014.887012>. Additionally, introduces a novel hypothesis test for evaluating covariate effects on the cure rate in mixture cure models, using MDC-based statistics. The methodology is described in Monroy-Castillo et al. (2025, manuscript submitted).