Package: MXM
Type: Package
Title: Feature Selection (Including Multiple Solutions) and Bayesian
        Networks
Version: 1.5.2
URL: http://mensxmachina.org
Date: 2021-09-21
Authors@R: c(person("Michail", "Tsagris", role = c("aut", "cre"), email = "mtsagris@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("Giorgos", "Borboudakis", role = c("ctb"), email = "borbudak@gmail.com"),
           person("Anna", "Roumpelaki", role = c("ctb"), email = "anna.roumpelaki@gmail.com") )
Author: Michail Tsagris [aut, cre],
  Ioannis Tsamardinos [aut, cph],
  Vincenzo Lagani [aut, cph],
  Giorgos Athineou [aut],
  Giorgos Borboudakis [ctb],
  Anna Roumpelaki [ctb]
Maintainer: Michail Tsagris <mtsagris@uoc.gr>
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). <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 (Accepted for publication) <doi:10.1109/TCBB.2020.3029952>.
License: GPL-2
Depends: R (>= 4.0)
Suggests: markdown, R.rsp
VignetteBuilder: knitr, R.rsp
Imports: methods, stats, utils, survival, MASS, graphics, ordinal,
        nnet, quantreg, lme4, foreach, doParallel, parallel, relations,
        Rfast, visNetwork, energy, geepack, knitr, dplyr, bigmemory,
        coxme, Rfast2, Hmisc
NeedsCompilation: no
Packaged: 2021-09-21 11:50:59 UTC; Michail
Repository: CRAN
Date/Publication: 2021-09-21 12:50:02 UTC
Built: R 4.0.5; ; 2022-04-21 15:32:39 UTC; windows
