person("Ioannis", "Tsamardinos", role = c("aut", "cph"), email = "tsamard.it@gmail.com"),
person("Vincenzo", "Lagani", role = c("aut", "cph"), email = "vlagani@yahoo.it"),
Author: Michail Tsagris [aut, cre], Ioannis Tsamardinos [aut, cph], Vincenzo Lagani [aut, cph], Giorgos Athineou [aut], Giorgos Borboudakis [ctb], Anna Roumpelaki [ctb]
Description: Feature selection methods for identifying 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. Reference: Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets, Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). Journal of Statistical Software, 80(7). <doi:10.18637/jss.v080.i07>.

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. Reference: Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets, Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). Journal of Statistical Software, 80(7). <doi:10.18637/jss.v080.i07>.
relations, Rfast, visNetwork, energy, geepack

relations, Rfast, visNetwork, energy, geepack, knitr, dplyr

relations, Rfast, visNetwork, energy, geepack, knitr, dplyr,

nnet, quantreg, lme4, foreach, doParallel, parallel, relations,
Rfast, visNetwork, energy, geepack, knitr, dplyr, bigmemory,

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: 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>.  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>. 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>. 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>. Tsagris, M. (2019). Bayesian Network Learning with the PC Algorithm: An Improved and Correct Variation, Journal of Applied Artificial Intelligence, 33(2):101-123. <doi:10.1080/08839514.2018.1526760>. Borboudakis, G. and Tsamardinos, I. (2019). Forward-Backward Selection with Early Dropping. Journal of Machine Learning Research 20: 1-39.