Package: MXM Type: Package Title: Feature Selection (Including Multiple Solutions) and Bayesian Networks Version: 1.5.8 Date: 2026-07-21 Authors@R: c( person("Konstantina", "Biza", role = c("aut"), email = "kbiza@csd.uoc.gr"), person("Ioannis", "Tsamardinos", role = c("aut", "cph"), email = "tsamard.it@gmail.com"), person("Vincenzo", "Lagani", role = c("aut", "cph"), email = "vlagani@yahoo.it"), person("Giorgos", "Athineou", role = c("aut"), email = "gioathineou@gmail.com"), person("Michail", "Tsagris", role = c("aut"), email = "mtsagris@uoc.gr"), person("Giorgos", "Borboudakis", role = c("ctb"), email = "borbudak@gmail.com"), person("Anna", "Roumpelaki", role = c("ctb"), email = "anna.roumpelaki@gmail.com"), person("Stavros", "Papadopoulos", role = c("cre"), email = "staurospapflor@gmail.com") ) Author: Konstantina Biza [aut], Ioannis Tsamardinos [aut, cph], Vincenzo Lagani [aut, cph], Giorgos Athineou [aut], Michail Tsagris [aut], Giorgos Borboudakis [ctb], Anna Roumpelaki [ctb], Stavros Papadopoulos [cre] Maintainer: Stavros Papadopoulos Description: 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). . b) Tsagris, M., Lagani, V. and Tsamardinos, I. (2018). "Feature selection for high-dimensional temporal data". BMC Bioinformatics, 19:17. . 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. . d) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018). "Efficient feature selection on gene expression data: Which algorithm to use?" BioRxiv. . e) Tsagris, M. (2019). "Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation". Applied Artificial Intelligence, 33(2):101-123. . f) Tsagris, M. and Tsamardinos, I. (2019). "Feature selection with the R package MXM". F1000Research 7: 1505. . g) Borboudakis, G. and Tsamardinos, I. (2019). "Forward-Backward Selection with Early Dropping". Journal of Machine Learning Research 20: 1-39. h) Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2022). "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. . License: GPL-2 Copyright: See inst/COPYRIGHTS. Depends: R (>= 4.0) Suggests: markdown, R.rsp, knitr VignetteBuilder: knitr, R.rsp Imports: methods, stats, utils, survival, MASS, graphics, ordinal, nnet, quantreg, lme4, foreach, doParallel, parallel, relations, Rfast, visNetwork, energy, geepack, dplyr, bigmemory, coxme, Rfast2, Hmisc NeedsCompilation: no Packaged: 2026-10-10 17:28:16 UTC; root Config/pak/sysreqs: cmake make libgsl0-dev libicu-dev libuv1-dev Repository: https://stavrospap1.r-universe.dev Date/Publication: 2026-09-01 14:59:12 UTC RemoteUrl: https://github.com/cran/MXM RemoteRef: HEAD RemoteSha: 60e9e8ead4105e23ed451a0ced9b57bb805aa37d