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
ROC and area under the curve
Cross-Validation for SES and MMPC
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
Generate random folds for cross-validation
Class "mases.output"
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
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
Class "MMPCoutput"
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
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"
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
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
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
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
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
Undirected path(s) between two nodes
Zero inflated Poisson regression
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