MXM (version 0.9.7)

MXM-package: This is an R package that currently implements feature selection methods for identifying minimal, statistically-equivalent and equally-predictive feature subsets. In addition, two algorithms for constructing the skeleton of a Bayesian network are included.

Description

'MXM' stands for Mens eX Machina, meaning 'Mind from the Machine' in Latin. The package provides source code for the SES algorithm and for some appropriate statistical conditional independence tests. (Fisher and Spearman corelation, G-square test are some examples. Currently the response variable can be univariate or multivariate Euclidean, proportions within 0 and 1, compositional data without zeros and ones, binary, nominal or ordinal multinomial, count data (handling also overdispersed and with more zeros than expected), longitudinal, clustered data, survival and case-control. Robust versions are also available in some cases and a K-fold cross validation is offered. Bayesian network related algorithms and ridge reression are also included. Read the package's help pages for more details.

MMPC and SES can handle even thousands of variables and for some tests, even many sample sizes of tens of thousands. The user is best advised to check his variables in the beginning. For some regressions, logistic and Poisson for example, we have used C++ codes for speed reasons. Thus no check is done for a variable with zero variance for instance. Something like colVars could be used in the first place to remove variables with zero variance.

Maintainers

Giorgos Athineou , Michail Tsagris

Details

Package:

MXM

Type:

Package

Version:

0.9.7

Date:

2016-12-20

License:

GPL-2

References

Tsamardinos, Brown and Aliferis (2006). The max-min hill-climbing Bayesian network structure learning algorithm. Machine learning, 65(1), 31-78.

I. Tsamardinos, V. Lagani and D. Pappas (2012) Discovering multiple, equivalent biomarker signatures. In proceedings of the 7th conference of the Hellenic Society for Computational Biology & Bioinformatics - HSCBB12.

Tsamardinos I., Aliferis C. F. and Statnikov, A. (2003). Time and sample efficient discovery of Markov blankets and direct causal relations. In Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining p. 673-678.

See Also

SES, MMPC, censIndCR,testIndFisher, testIndLogistic, gSquare, testIndRQ