MXM

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.