MXM package - RDocumentation

Conditional independence tests for survival data

Conditional independence test for survival data

BIC based forward selection with generalised linear models

Variable selection in generalised linear models with forward selection based on BIC

Many simple beta regressions

Many simple beta regressions.

Beta regression

ROC and area under the curve

Cross-Validation for SES and MMPC

BIC based forward selection

Variable selection in regression models with forward selection using BIC

Correlation based tests with and without permutation p-value

Fisher conditional independence test for continuous class variables with and without permutation based p-value

Transformation of a DAG into an essential graph

Transforms a DAG into an essential graph

Backward selection regression

Variable selection in regression models with backward selection

Backward selection with generalised linear regression models

Variable selection in generalised linear regression models with backward selection

Generate random folds for cross-validation

mases.output-class

Class "mases.output"

mammpc.output-class

Class "mammpc.output"

Forward selection with generalised linear regression models

Variable selection in generalised linear regression models with forward selection

Constraint based feature selection algorithms for multiple datasets

ma.ses: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with multiple datasets ma.mmpc: Feature selection algorithm for identifying minimal feature subsets with multiple datasets

Check Markov equivalence of two DAGs

Forward selection with linear regression models

Variable selection in linear regression models with forward selection

Forward selection regression

Variable selection in regression models with forward selection

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

The skeleton of a Bayesian network as produced by MMHC

MMPCoutput-class

Class "MMPCoutput"

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.

Orientation rules for the PC algorithm

The orientations part of the PC algorithm.

MMPC solution paths for many combinations of hyper-parameters

The max-min Markov blanket algorithm

Max-min Markov blanket algorithm

MMPC.temporal.output-class

Class "MMPC.temporal.output"

MXM-internal

Internal MXM Functions

CondInditional independence tests

MXM Conditional independence tests

Partial correlation between two variables

Partial correlation

Neighbours of nodes in an undirected graph

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

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

Interactive plot of an (un)directed graph

Data simulation from a DAG

Simulation of data from DAG (directed acyclic graph)

Regression models based on SES and MMPC outputs

Regression model(s) obtained from SES or MMPC

Regression models fitting

Regression modelling

Ridge regression coefficients plot

Ridge regression

Skeleton of the PC algorithm

The skeleton of a Bayesian network produced by the PC algorithm

Cross-validation for ridge regression

Cross validation for the ridge regression

Permutation based p-value for the Pearson correlation coefficient

Ridge regression

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

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

SESoutput-class

Class "SESoutput"

Conditional independence test for proportions/percentages

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

Correlation based conditonal independence tests

Fisher and Spearman conditional independence test for continuous class variables

Plot of longitudinal data

Conditional independence tests for sucess rates

Binomial regression conditional independence test for success rates (binomial)

Conditional independence test for case control data

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

SES.temporal.output-class

Class "SES.temporal.output"

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

Undirected path(s) between two nodes

Zero inflated Poisson regression

Conditional independence tests for count data

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

Many simple zero inflated Poisson regressions

Many simple zero inflated Poisson regressions.

Conditional independence tests for continous univariate and multivariate data

Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables

MXM (version 0.9.7)

Discovering Multiple, Statistically-Equivalent Signatures

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Functions in MXM (0.9.7)