MXM 1.4.4
Feature Selection (Including Multiple Solutions) and Bayesian Networks
Released Jun 19, 2019 by Michail Tsagris
This package cannot yet be used with Renjin it depends on other packages which are not available:
quantreg 5.41 and
knitr 1.23
An
older version of this package is
more compatible with Renjin.
Dependencies
quantreg 5.41
knitr 1.23
dplyr 0.8.2
nnet 7.3-12
MASS 7.3-51.4
survival 2.44-1.1
foreach 1.4.4
Rfast 1.9.4
visNetwork 2.0.7
ordinal 2019.4-25
coxme 2.2-10
lme4 1.1-21
geepack 1.2-1
bigmemory 4.5.33
doParallel 1.0.14
relations 0.6-8
energy 1.7-5
Rfast2 0.0.2
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) Borboudakis, G. and Tsamardinos, I. (2019). Forward-Backward Selection with Early Dropping. Journal of Machine Learning Research 20: 1-39.
