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: 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). . Tsagris, M., Lagani, V. and Tsamardinos, I. (2018). Feature selection for high-dimensional temporal data. BMC Bioinformatics, 19:17. . 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. . Tsagris, M., Papadovasilakis, Z., Lakiotaki, K. and Tsamardinos, I. (2018). Efficient feature selection on gene expression data: Which algorithm to use? BioRxiv. . Tsagris, M. (2019). Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation, Journal of Applied Artificial Intelligence, 33(2):101-123. . Borboudakis, G. and Tsamardinos, I. (2019). Forward-Backward Selection with Early Dropping. Journal of Machine Learning Research 20: 1-39.


Reference manual

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1.4.3 by Michail Tsagris, 21 days ago


Browse source code at https://github.com/cran/MXM

Authors: Michail Tsagris [aut, cre] , Ioannis Tsamardinos [aut, cph] , Vincenzo Lagani [aut, cph] , Giorgos Athineou [aut] , Giorgos Borboudakis [ctb] , Anna Roumpelaki [ctb]

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning, gRaphical Models in R

GPL-2 license

Imports methods, stats, utils, survival, MASS, graphics, ordinal, nnet, quantreg, lme4, foreach, doParallel, parallel, relations, Rfast, visNetwork, energy, geepack, knitr, dplyr, bigmemory, coxme, Rfast2

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