MXM: Feature Selection (Including Multiple Solutions) and Bayesian Networks

Many feature selection methods for a wide range of response variables, including minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin. References: a) Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets. Journal of Statistical Software, 80(7). <doi:10.18637/jss.v080.i07>. b) Tsagris, M., Lagani, V. and Tsamardinos, I. (2018). Feature selection for high-dimensional temporal data. BMC Bioinformatics, 19:17. <doi:10.1186/s12859-018-2023-7>. c) Tsagris, M., Borboudakis, G., Lagani, V. and Tsamardinos, I. (2018). Constraint-based causal discovery with mixed data. International Journal of Data Science and Analytics, 6(1): 19-30. <doi:10.1007/s41060-018-0097-y>. d) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018). Efficient feature selection on gene expression data: Which algorithm to use? BioRxiv. <doi:10.1101/431734>. e) Tsagris, M. (2019). Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation. Applied Artificial Intelligence, 33(2):101-123. <doi:10.1080/08839514.2018.1526760>. f) Tsagris, M. and Tsamardinos, I. (2019). Feature selection with the R package MXM. F1000Research 7: 1505. <doi:10.12688/f1000research.16216.2>. g) Borboudakis, G. and Tsamardinos, I. (2019). Forward-Backward Selection with Early Dropping. Journal of Machine Learning Research 20: 1-39. h) The gamma-OMP algorithm for feature selection with application to gene expression data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 19(2): 1214-1224. <doi:10.1109/TCBB.2020.3029952>.

Version:	1.5.5
Depends:	R (≥ 4.0)
Imports:	methods, stats, utils, survival, MASS, graphics, ordinal, nnet, quantreg, lme4, foreach, doParallel, parallel, relations, Rfast, visNetwork, energy, geepack, knitr, dplyr, bigmemory, coxme, Rfast2, Hmisc
Suggests:	markdown, R.rsp
Published:	2022-08-25
Author:	Konstantina Biza [aut, cre], Ioannis Tsamardinos [aut, cph], Vincenzo Lagani [aut, cph], Giorgos Athineou [aut], Michail Tsagris [aut], Giorgos Borboudakis [ctb], Anna Roumpelaki [ctb]
Maintainer:	Konstantina Biza <kbiza at csd.uoc.gr>
License:	GPL-2
URL:	http://mensxmachina.org
NeedsCompilation:	no
Citation:	MXM citation info
In views:	GraphicalModels
CRAN checks:	MXM results

Documentation:

Reference manual:	MXM.pdf
Vignettes:	Tutorial: Feature selection with the MMPC algorithm Tutorial: Feature selection with the SES algorithm Guide on performing feature selection with the R package MXM Discovering Statistically-Equivalent Feature Subsets with MXM A very brief guide to using MXM

Downloads:

Package source:	MXM_1.5.5.tar.gz
Windows binaries:	r-devel: MXM_1.5.5.zip, r-release: MXM_1.5.5.zip, r-oldrel: MXM_1.5.5.zip
macOS binaries:	r-release (arm64): MXM_1.5.5.tgz, r-oldrel (arm64): MXM_1.5.5.tgz, r-release (x86_64): MXM_1.5.5.tgz, r-oldrel (x86_64): MXM_1.5.5.tgz
Old sources:	MXM archive

Linking:

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