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MXM (version 0.9.3)

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

0.9.3

License

GPL-2

Maintainer

Michail Tsagris

Last Published

July 10th, 2016

Functions in MXM (0.9.3)

Ancestors of a node in a directed graph

Returns and plots, if asked, the ancestors of a node (or variable)
Transformation of a DAG into an essential graph

Transforms a DAG into an essential graph
BIC based forward selection with generalised linear models

Variable selection in generalised linear models with forward selection based on BIC
Descendants of a node in a directed graph

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

Variable selection in regression models with forward selection
Cross-Validation for SES and MMPC

Cross-Validation for SES and MMPC
Correlation based tests with and without permutation p-value

Fisher conditional independence test for continuous class variables with and without permutation based p-value
Backward selection with generalised linear regression models

Variable selection in generalised linear regression models with backward selection
Conditional independence tests for survival data

Conditional independence test for survival data
BIC based forward selection

Variable selection in regression models with forward selection using BIC
MMPC.temporal.output-class

Class "MMPC.temporal.output"
MMPCoutput-class

Class "MMPCoutput"
Forward selection with linear regression models

Variable selection in linear regression models with forward selection
MXM-internal

Internal MXM Functions
MMPC solution paths for many combinations of hyper-parameters

MMPC solution paths for many combinations of hyper-parameters
The max-min Markov blanket algorithm

Max-min Markov blanket algorithm
Skeleton of the max-min hill-climbing (MMHC) algorithm

The skeleton of a Bayesian network as produced by MMHC
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.
Forward selection with generalised linear regression models

Variable selection in generalised linear regression models with forward selection
Markov Blanket of a node in a directed graph

Returns and plots, if asked, the Markov blanket of a node (or variable)
Skeleton of the PC algorithm

The skeleton of a Bayesian network produced by the PC algorithm
Ridge regression

Ridge regression
Orientation rules for the PC algorithm

The orientations part of the PC algorithm.
Partial correlation between two variables

Partial correlation
Permutation based p-value for the Pearson correlation coefficient

Permutation based p-value for the Pearson correlation coefficient
Regression models fitting

Regression modelling
Plot of an (un)directed graph

Plot of an (un)directed graph
Ridge regression coefficients plot

Ridge regression
CondInditional independence tests

MXM Conditional independence tests
Neighbours of nodes in an undirected graph

Returns and plots, if asked, the node(s) and their neighbour(s), if there are any.
Plot of longitudinal data

Plot of longitudinal data
Conditional independence test for case control data

Conditional independence test based on conditional logistic regression for case control studies
Conditional independence tests for sucess rates

Binomial regression conditional independence test for success rates (binomial)
SESoutput-class

Class "SESoutput"
Cross-validation for ridge regression

Cross validation for the ridge regression
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
SES.temporal.output-class

Class "SES.temporal.output"
Constraint based feature selection algorithms for longitudinal and clustered 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
Regression models based on SES and MMPC outputs

Regression model(s) obtained from SES or MMPC
Conditional independence test for proportions/percentages

Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors
Transitive closure of an adjacency matrix

Returns the transitive closure of an adjacency matrix
Conditional independence test for continuous, binary and count data with thousands of samples

Conditional independence test for continuous, binary and discrete (counts) variables with thousands of observations
Conditional independence tests for continous univariate and multivariate data

Linear regression conditional independence test for continous univariate and multivariate response variables
Conditional independence test for binary, categorical or ordinal data

Conditional independence test for binary, categorical or ordinal class variables
Conditional independence test for longitudinal and clustered data

Linear mixed models conditional independence test for longitudinal class variables
Correlation based conditonal independence tests

Fisher and Spearman conditional independence test for continuous class variables
Undirected path(s) between two nodes

Undirected path(s) between two nodes
Conditional independence tests for count data

Regression conditional independence test for discrete (counts) class dependent variables