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). . 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. .