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MXM (version 0.8.7)

Discovering Multiple, Statistically-Equivalent Signatures

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.

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Version

Install

install.packages('MXM')

Monthly Downloads

1,130

Version

0.8.7

License

GPL-2

Maintainer

Michail Tsagris

Last Published

May 23rd, 2016

Functions in MXM (0.8.7)

MMPC.temporal.output-class

Class "MMPC.temporal.output"
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.
bic.glm.fsreg

Variable selection in generalised linear regression models with forward selection
Orientation rules for the PC algorithm

The orientations part of the PC algorithm.
MMPCoutput-class

Class "MMPCoutput"
SESoutput-class

Class "SESoutput"
Permutation based p-value for the Pearson correlation coefficient

Permutation based p-value for the Pearson correlation coefficient
Constraint based feature selection algorithms

SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures. MMPC: Feature selection algorithm for identifying minimal feature subsets.
dag2eg

Transforms a DAG into an essential graph
Constraint based feature selection algorithms for longitudinal data

SES.temporal: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures. MMPC.temporal: Feature selection algorithm for identifying minimal feature subsets.
testIndGLMM

Linear mixed models conditional independence test for longitudinal class variables
reg.fit

Regression modelling
ridgereg.cv

Cross validation for the ridge regression
rdag

G square conditional independence test for discrete data
CondIndTests

MXM Conditional Independence Tests
testIndBeta

Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors
ridge.plot

Ridge regression
mmhc.skel

The skeleton of a Bayesian network produced by MMHC
MXM-internal

Internal MXM Functions
Correlation based tests

Fisher and Spearman conditional independence test for continuous class variables
cv.ses

Cross-Validation for SES
SES.temporal.output-class

Class "SES.temporal.output"
ridge.reg

Ridge regression
Linear regression models

Linear regression conditional independence test for continous univariate and multivariate response variables
transitiveClosure

Returns the transitive closure of a graph.
model

Regression model(s) obtained from SES
testIndClogit

Conditional independence test based on conditional logistic regression for case control studies
Survival regression

Conditional independence test for survival data
testIndSpeedglm

Conditional independence test for continuous, binary and discrete (counts) variables with thousands of observations.
undir.path

Undirected path(s) between two nodes.
Count data regression models

Regression conditional independence test for discrete (counts) class dependent variables
Skeleton of the PC algorithm

The skeleton of a Bayesian network produced by the PC algorithm
plota

Plot of an (un)directed graph
nei

Returns and plots, if asked, the node(s) and their neighbour(s), if there are any.
findAncestors

Returns and plots, if asked, the ancestors of a node (or variable)
mb

Returns and plots, if asked, the Markov blanket of a node (or variable).
findDescendants

Returns and plots, if asked, the descendants of a node (or variable)
mmmb

mmmb: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures.
glm.fsreg

Variable selection in generalised linear regression models with forward selection
fs.reg

Variable selection in regression models with forward selection
glm.bsreg

Variable selection in generalised linear regression models with backward selection
lm.fsreg

Variable selection in linear regression models with forward selection
bic.fsreg

Variable selection in regression models with forward selection using BIC
gSquare

G square conditional independence test for discrete data
testIndLogistic

Conditional independence test for binary, categorical or ordinal class variables