# Feature Selection (Including Multiple Solutions) and Bayesian Networks

## Documentation for package ‘MXM’ version 1.3.8

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

A B C D E F G I K L M N O P Q R S T U W Z

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.

## -- A --

acc.mxm | Cross-Validation for SES and MMPC
acc_multinom.mxm | Cross-Validation for SES and MMPC
apply_ideq | Internal MXM Functions
apply_ideq.glmm | Internal MXM Functions
apply_ideq.ma | Internal MXM Functions
auc | ROC and AUC
auc.mxm | Cross-Validation for SES and MMPC

## -- B --

bbc | Bootstrap bias correction for the performance of the cross-validation procedure
beta.bsreg | Internal MXM Functions
beta.fsreg | Internal MXM Functions
beta.fsreg_2 | Internal MXM Functions
beta.mod | Beta regression
beta.mxm | Cross-Validation for SES and MMPC
beta.regs | Many simple beta regressions.
betamle.wei | Internal MXM Functions
bic.betafsreg | Internal MXM Functions
bic.clogit.fsreg | Internal MXM Functions
bic.fsreg | Variable selection in regression models with forward selection using BIC
bic.gammafsreg | Variable selection in generalised linear models with forward selection based on BIC
bic.glm.fsreg | Variable selection in generalised linear models with forward selection based on BIC
bic.mm.fsreg | Variable selection in generalised linear models with forward selection based on BIC
bic.normlog.fsreg | Variable selection in generalised linear models with forward selection based on BIC
bic.tobit.fsreg | Internal MXM Functions
bic.wr.fsreg | Internal MXM Functions
bic.zipfsreg | Internal MXM Functions
big.bs.g2 | Internal MXM Functions
big.fbed.g2 | Internal MXM Functions
big.fbed.reg | Forward Backward Early Dropping selection regression for big data
big.gomp | Generalised orthogonal matching pursuit(gOMP) for big data
big.gomp.path | Generalised orthogonal matching pursuit(gOMP) for big data
big.model | Internal MXM Functions
big.score.univregs | Univariate regression based tests
bn.skel.utils | Utilities for the skeleton of a (Bayesian) Network
bn.skel.utils2 | Utilities for the skeleton of a (Bayesian) Network
bs.g2 | Internal MXM Functions
bs.reg | Variable selection in regression models with backward selection
bsreg.big | Internal MXM Functions

## -- C --

cat.ci | Conditional independence test for continuous class variables with and without permutation based p-value
cat_condis | Internal MXM Functions
censIndCR | Conditional independence test for survival data
censIndER | Conditional independence test for survival data
censIndWR | Conditional independence test for survival data
certificate.of.exclusion | Certificate of exclusion from the selected variables set using SES or MMPC
certificate.of.exclusion2 | Certificate of exclusion from the selected variables set using SES or MMPC
ci.fast | Symmetric conditional independence test with mixed data
ci.fast2 | Symmetric conditional independence test with mixed data
ci.mm | Symmetric conditional independence test with mixed data
ci.mm2 | Symmetric conditional independence test with mixed data
ci.mxm | Cross-Validation for SES and MMPC
ciwr.mxm | Cross-Validation for SES and MMPC
clogit.fsreg | Internal MXM Functions
clogit.fsreg_2 | Internal MXM Functions
clogit.mxm | Cross-Validation for SES and MMPC
comb_condis | Internal MXM Functions
compare_p_values | Internal MXM Functions
cond.regs | Conditional independence regression based tests
condi | Conditional independence test for continuous class variables with and without permutation based p-value
condi.perm | Internal MXM Functions
CondIndTests | MXM Conditional independence tests
condis | Many conditional independence tests counting the number of times a possible collider d-separates two nodes
conf.edge.lower | Lower limit of the confidence of an edge
cor.drop1 | Drop all possible single terms from a model using the partial correlation
corfbed.network | Network construction using the partial correlation based forward regression of FBED
corfs.network | Network construction using the partial correlation based forward regression of FBED
corgraph | Graph of unconditional associations
coxph.mxm | Cross-Validation for SES and MMPC
cv.gomp | Cross-Validation for gOMP
cv.mmpc | Cross-Validation for SES and MMPC
cv.permmmpc | Cross-Validation for SES and MMPC
cv.permses | Cross-Validation for SES and MMPC
cv.ses | Cross-Validation for SES and MMPC
cv.waldmmpc | Cross-Validation for SES and MMPC
cv.waldses | Cross-Validation for SES and MMPC
cvlogit.cv.ses | Internal MXM Functions
cvmmpc.par | Internal MXM Functions
cvpermmmpc.par | Internal MXM Functions
cvpermses.par | Internal MXM Functions
cvses.par | Internal MXM Functions
cvwaldmmpc.par | Internal MXM Functions
cvwaldses.par | Internal MXM Functions

## -- D --

dag2eg | Transforms a DAG into an essential graph
dag_to_eg | Internal MXM Functions
disctor_condis | Internal MXM Functions
dist.condi | Conditional independence test for continuous class variables with and without permutation based p-value

## -- E --

ebic.beta.bsreg | Internal MXM Functions
ebic.bsreg | Backward selection regression using the eBIC
ebic.cr.bsreg | Internal MXM Functions
ebic.fbed.beta | Internal MXM Functions
ebic.fbed.cr | Internal MXM Functions
ebic.fbed.glm | Internal MXM Functions
ebic.fbed.glmm | Internal MXM Functions
ebic.fbed.glmm.cr | Internal MXM Functions
ebic.fbed.glmm.ordinal | Internal MXM Functions
ebic.fbed.glmm.ordinal.reps | Internal MXM Functions
ebic.fbed.glmm.reps | Internal MXM Functions
ebic.fbed.lm | Internal MXM Functions
ebic.fbed.lmm | Internal MXM Functions
ebic.fbed.lmm.reps | Internal MXM Functions
ebic.fbed.mmreg | Internal MXM Functions
ebic.fbed.multinom | Internal MXM Functions
ebic.fbed.nb | Internal MXM Functions
ebic.fbed.ordinal | Internal MXM Functions
ebic.fbed.tobit | Internal MXM Functions
ebic.fbed.wr | Internal MXM Functions
ebic.fbed.zip | Internal MXM Functions
ebic.glm.bsreg | Internal MXM Functions
ebic.glmm.bsreg | Backward selection regression for GLMM using the eBIC
ebic.glmm.cr.bsreg | Internal MXM Functions
ebic.glmm.ordinal.reps.bsreg | Internal MXM Functions
ebic.glmm.reps.bsreg | Internal MXM Functions
ebic.lm.bsreg | Internal MXM Functions
ebic.mm.bsreg | Internal MXM Functions
ebic.model | Internal MXM Functions
ebic.multinom.bsreg | Internal MXM Functions
ebic.ordinal.bsreg | Internal MXM Functions
ebic.regs | eBIC for many regression models
ebic.tobit.bsreg | Internal MXM Functions
ebic.univregs | Univariate regression based tests
ebic.wr.bsreg | Internal MXM Functions
ebic.zip.bsreg | Internal MXM Functions
ebicScore | Internal MXM Functions
equivdags | Check Markov equivalence of two DAGs
euclid_sens.spec.mxm | Cross-Validation for SES and MMPC

## -- F --

fbed.ebic | Internal MXM Functions
fbed.g2 | Internal MXM Functions
fbed.gee.reg | Forward Backward Early Dropping selection regression with GEE
fbed.geeglm | Internal MXM Functions
fbed.geeglm.reps | Internal MXM Functions
fbed.geelm | Internal MXM Functions
fbed.geelm.reps | Internal MXM Functions
fbed.glmm | Internal MXM Functions
fbed.glmm.cr | Internal MXM Functions
fbed.glmm.ordinal | Internal MXM Functions
fbed.glmm.ordinal.reps | Internal MXM Functions
fbed.glmm.reg | Forward Backward Early Dropping selection regression with GLMM
fbed.glmm.reps | Internal MXM Functions
fbed.lmm | Internal MXM Functions
fbed.lmm.reps | Internal MXM Functions
fbed.lr | Internal MXM Functions
fbed.ordgee | Internal MXM Functions
fbed.ordgee.reps | Internal MXM Functions
fbed.reg | Forward Backward Early Dropping selection regression
fbedreg.bic | Incremental BIC values and final regression model of the FBED algorithm
findAncestors | Returns and plots, if asked, the descendants or ancestors of one or all node(s) (or variable(s))
findDescendants | Returns and plots, if asked, the descendants or ancestors of one or all node(s) (or variable(s))
fs.reg | Variable selection in regression models with forward selection
fs.reg_2 | Internal MXM Functions
fscore.mxm | Cross-Validation for SES and MMPC

## -- G --

gammabsreg | Variable selection in generalised linear regression models with backward selection
gammafsreg | Variable selection in generalised linear regression models with forward selection
gammafsreg_2 | Internal MXM Functions
gee.ci.mm | Symmetric conditional independence test with clustered data
gee.condregs | Conditional independence regression based tests
gee.mmhc.skel | The skeleton of a Bayesian network as produced by MMHC
gee.pc.skel | The skeleton of a Bayesian network produced by the PC algorithm
gee.univregs | Univariate regression based tests
generatefolds | Generate random folds for cross-validation
glm.bsreg | Variable selection in generalised linear regression models with backward selection
glm.fsreg | Variable selection in generalised linear regression models with forward selection
glm.fsreg_2 | Internal MXM Functions
glm.mxm | Cross-Validation for SES and MMPC
glmm.bsreg | Backward selection regression for GLMM
glmm.ci.mm | Symmetric conditional independence test with clustered data
glmm.condregs | Conditional independence regression based tests
glmm.cr.bsreg | Internal MXM Functions
glmm.mmhc.skel | The skeleton of a Bayesian network as produced by MMHC
glmm.ordinal.bsreg | Internal MXM Functions
glmm.ordinal.reps.bsreg | Internal MXM Functions
glmm.pc.skel | The skeleton of a Bayesian network produced by the PC algorithm
glmm.univregs | Univariate regression based tests
gomp | Generalised orthogonal matching pursuit (gOMP)
gomp.path | Generalised orthogonal matching pursuit (gOMP)
gomp2 | Internal MXM Functions
group.mvbetas | Calculation of the constant and slope for each subject over time
gSquare | G-square conditional independence test for discrete data

## -- I --

iamb | IAMB variable selection
iamb.betabs | Internal MXM Functions
iamb.bs | IAMB backward selection phase
iamb.gammabs | Internal MXM Functions
iamb.glmbs | Internal MXM Functions
iamb.normlogbs | Internal MXM Functions
iamb.tobitbs | Internal MXM Functions
iamb.zipbs | Internal MXM Functions
ida | Total causal effect of a node on another node
IdentifyEquivalence | Internal MXM Functions
IdentifyEquivalence.gee | Internal MXM Functions
IdentifyEquivalence.glmm | Internal MXM Functions
IdentifyEquivalence.ma | Internal MXM Functions
identifyTheEquivalent | Internal MXM Functions
identifyTheEquivalent.gee | Internal MXM Functions
identifyTheEquivalent.glmm | Internal MXM Functions
identifyTheEquivalent.ma | Internal MXM Functions
internaliamb.betabs | Internal MXM Functions
internaliamb.binombs | Internal MXM Functions
internaliamb.gammabs | Internal MXM Functions
internaliamb.lmbs | Internal MXM Functions
internaliamb.mmbs | Internal MXM Functions
internaliamb.normlogbs | Internal MXM Functions
internaliamb.poisbs | Internal MXM Functions
internaliamb.tobitbs | Internal MXM Functions
internaliamb.zipbs | Internal MXM Functions
Internalmammpc | Internal MXM Functions
Internalmases | Internal MXM Functions
InternalMMPC | Internal MXM Functions
InternalMMPC.gee | Internal MXM Functions
InternalMMPC.glmm | Internal MXM Functions
InternalMMPC.timeclass | Internal MXM Functions
InternalSES | Internal MXM Functions
InternalSES.gee | Internal MXM Functions
InternalSES.glmm | Internal MXM Functions
is.dag | Check whether a directed graph is acyclic
is.sepset | Internal MXM Functions

## -- K --

kfbed.gee.reg | Internal MXM Functions
kfbed.glmm.reg | Internal MXM Functions
kfbed.reg | Internal MXM Functions

## -- L --

lm.fsreg | Variable selection in linear regression models with forward selection
lm.fsreg_2 | Internal MXM Functions
lm.mxm | Cross-Validation for SES and MMPC
lmm.bsreg | Internal MXM Functions
lmrob.mxm | Cross-Validation for SES and MMPC
local.mmhc.skel | Skeleton (local) around a node of the MMHC algorithm

## -- M --

ma.mmpc | 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
ma.ses | 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
mae.mxm | Cross-Validation for SES and MMPC
mammpc.output | Class '"mammpc.output"'
mammpc.output-class | Class '"mammpc.output"'
mammpc.output-method | Class '"mammpc.output"'
mases.output | Class '"mases.output"'
mases.output-class | Class '"mases.output"'
mases.output-method | Class '"mases.output"'
max_min_assoc | Internal MXM Functions
max_min_assoc.gee | Internal MXM Functions
max_min_assoc.glmm | Internal MXM Functions
max_min_assoc.ma | Internal MXM Functions
mb | Returns the Markov blanket of a node (or variable)
mci.mxm | Cross-Validation for SES and MMPC
min_assoc | Internal MXM Functions
min_assoc.gee | Internal MXM Functions
min_assoc.glmm | Internal MXM Functions
min_assoc.ma | Internal MXM Functions
mmhc.skel | The skeleton of a Bayesian network as produced by MMHC
mmmb | Max-min Markov blanket algorithm
MMPC | SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets
MMPC.gee | SES.glmm/SES.gee: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with correlated data
mmpc.gee.model | Generalised linear mixed model(s) based obtained from glmm SES or MMPC
MMPC.gee.output | Class '"MMPC.gee.output"'
MMPC.gee.output-class | Class '"MMPC.gee.output"'
MMPC.gee.output-method | Class '"MMPC.gee.output"'
mmpc.gee2 | mmpc.glmm2/mmpc.gee2: Fast Feature selection algorithm for identifying minimal feature subsets with correlated data
MMPC.glmm | SES.glmm/SES.gee: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with correlated data
mmpc.glmm.model | Generalised linear mixed model(s) based obtained from glmm SES or MMPC
MMPC.glmm.output | Class '"MMPC.glmm.output"'
MMPC.glmm.output-class | Class '"MMPC.glmm.output"'
MMPC.glmm.output-method | Class '"MMPC.glmm.output"'
mmpc.glmm2 | mmpc.glmm2/mmpc.gee2: Fast Feature selection algorithm for identifying minimal feature subsets with correlated data
mmpc.model | Regression model(s) obtained from SES or MMPC
mmpc.or | Bayesian Network construction using a hybrid of MMPC and PC
mmpc.path | MMPC solution paths for many combinations of hyper-parameters
MMPC.timeclass | Feature selection using SES and MMPC for classifiication with longitudinal data
mmpc.timeclass.model | Regression model(s) obtained from SES.timeclass or MMPC.timeclass
mmpc2 | A fast version of MMPC
mmpcbackphase | Backward phase of MMPC
MMPCoutput | Class '"MMPCoutput"'
MMPCoutput-class | Class '"MMPCoutput"'
MMPCoutput-method | Class '"MMPCoutput"'
modeler | Generic regression modelling function
mse.mxm | Cross-Validation for SES and MMPC
multinom.mxm | Cross-Validation for SES and MMPC

## -- N --

nb.mxm | Cross-Validation for SES and MMPC
nbdev.mxm | Cross-Validation for SES and MMPC
nchoosek | Internal MXM Functions
nei | Returns the node(s) and their neighbour(s), if there are any.
Ness | Effective sample size for G^2 test in BNs with case control data
normlog.bsreg | Variable selection in generalised linear regression models with backward selection
normlog.fsreg | Variable selection in generalised linear regression models with forward selection

## -- O --

ord.resid | Probability residual of ordinal logistic regreession
ordinal.mxm | Cross-Validation for SES and MMPC
ordinal.reg | Generalised ordinal regression
ord_mae.mxm | Cross-Validation for SES and MMPC

## -- P --

partialcor | Partial correlation
pc.con | The skeleton of a Bayesian network produced by the PC algorithm
pc.or | The orientations part of the PC algorithm.
pc.skel | The skeleton of a Bayesian network produced by the PC algorithm
pc.skel.boot | The skeleton of a Bayesian network produced by the PC algorithm
pearson_condis | Internal MXM Functions
pearson_condis.rob | Internal MXM Functions
perm.apply_ideq | Internal MXM Functions
perm.betaregs | Many simple beta regressions.
perm.IdentifyEquivalence | Internal MXM Functions
perm.identifyTheEquivalent | Internal MXM Functions
perm.Internalmmpc | Internal MXM Functions
perm.mmpc | SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets
perm.ses | SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets
perm.univariateScore | Internal MXM Functions
perm.univregs | Univariate regression based tests
perm.zipregs | Many simple zero inflated Poisson regressions.
permBeta | Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors
permBinom | Binomial regression conditional independence test for success rates (binomial)
permClogit | Conditional independence test based on conditional logistic regression for case control studies
permcor | Permutation based p-value for the Pearson correlation coefficient
permcorrels | Permutation based p-value for the Pearson correlation coefficient
permCR | Conditional independence test for survival data
permDcor | Fisher and Spearman conditional independence test for continuous class variables
permER | Conditional independence test for survival data
permFisher | Fisher and Spearman conditional independence test for continuous class variables
permGamma | Regression conditional independence test for positive response variables.
permgSquare | G-square conditional independence test for discrete data
permIGreg | Regression conditional independence test for positive response variables.
permLogistic | Conditional independence test for binary, categorical or ordinal class variables
permMMFisher | Fisher and Spearman conditional independence test for continuous class variables
permMMReg | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
permMultinom | Conditional independence test for binary, categorical or ordinal class variables
permMVreg | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
permNB | Regression conditional independence test for discrete (counts) class dependent variables
permNormLog | Regression conditional independence test for positive response variables.
permOrdinal | Conditional independence test for binary, categorical or ordinal class variables
permPois | Regression conditional independence test for discrete (counts) class dependent variables
permReg | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
permRQ | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
permTobit | Conditional independence test for survival data
permWR | Conditional independence test for survival data
permZIP | Regression conditional independence test for discrete (counts) class dependent variables
pi0est | Estimation of the percentage of Null p-values
plot-method | Class '"MMPC.gee.output"'
plot-method | Class '"MMPC.glmm.output"'
plot-method | Class '"MMPCoutput"'
plot-method | Class '"SES.gee.output"'
plot-method | Class '"SES.glmm.output"'
plot-method | Class '"SESoutput"'
plot-method | Class '"mammpc.output"'
plot-method | Class '"mases.output"'
plotnetwork | Interactive plot of an (un)directed graph
pois.mxm | Cross-Validation for SES and MMPC
poisdev.mxm | Cross-Validation for SES and MMPC
proc_time-class | Internal MXM Functions
pval.mixbeta | Fit a mixture of beta distributions in p-values
pve.mxm | Cross-Validation for SES and MMPC

## -- Q --

quasibinom.bsreg | Internal MXM Functions
quasibinom.fsreg | Internal MXM Functions
quasibinom.fsreg_2 | Internal MXM Functions
quasipois.bsreg | Internal MXM Functions
quasipois.fsreg | Internal MXM Functions
quasipois.fsreg_2 | Internal MXM Functions

## -- R --

R0 | Internal MXM Functions
R1 | Internal MXM Functions
R2 | Internal MXM Functions
R3 | Internal MXM Functions
rdag | Simulation of data from DAG (directed acyclic graph)
rdag2 | Simulation of data from DAG (directed acyclic graph)
read.big.data | Forward Backward Early Dropping selection regression
reg.fit | Regression modelling
regbeta | Internal MXM Functions
regbetawei | Internal MXM Functions
regzip | Internal MXM Functions
regzipwei | Internal MXM Functions
ridge.plot | Ridge regression
ridge.reg | Ridge regression
ridgereg.cv | Cross validation for the ridge regression
rint.regs | Univariate regression based tests
rmdag | Simulation of data from DAG (directed acyclic graph)
rq.mxm | Cross-Validation for SES and MMPC

## -- S --

score.univregs | Univariate regression based tests
SES | 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.gee | SES.glmm/SES.gee: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with correlated data
ses.gee.model | Generalised linear mixed model(s) based obtained from glmm SES or MMPC
SES.gee.output | Class '"SES.gee.output"'
SES.gee.output-class | Class '"SES.gee.output"'
SES.gee.output-method | Class '"SES.gee.output"'
SES.glmm | SES.glmm/SES.gee: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures with correlated data
ses.glmm.model | Generalised linear mixed model(s) based obtained from glmm SES or MMPC
SES.glmm.output | Class '"SES.glmm.output"'
SES.glmm.output-class | Class '"SES.glmm.output"'
SES.glmm.output-method | Class '"SES.glmm.output"'
ses.model | Regression model(s) obtained from SES or MMPC
SES.timeclass | Feature selection using SES and MMPC for classifiication with longitudinal data
ses.timeclass.model | Regression model(s) obtained from SES.timeclass or MMPC.timeclass
SESoutput | Class '"SESoutput"'
SESoutput-class | Class '"SESoutput"'
SESoutput-method | Class '"SESoutput"'
shd | Structural Hamming distance between two partially oriented DAGs
supervised.pca | Supervised PCA

## -- T --

tc.plot | Plot of longitudinal data
test.maker | Internal MXM Functions
testIndBeta | Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors
testIndBinom | Binomial regression conditional independence test for success rates (binomial)
testIndClogit | Conditional independence test based on conditional logistic regression for case control studies
testIndFisher | Fisher and Spearman conditional independence test for continuous class variables
testIndGamma | Regression conditional independence test for positive response variables.
testIndGEEGamma | Linear mixed models conditional independence test for longitudinal class variables
testIndGEELogistic | Linear mixed models conditional independence test for longitudinal class variables
testIndGEENormLog | Linear mixed models conditional independence test for longitudinal class variables
testIndGEEOrdinal | Linear mixed models conditional independence test for longitudinal class variables
testIndGEEPois | Linear mixed models conditional independence test for longitudinal class variables
testIndGEEReg | Linear mixed models conditional independence test for longitudinal class variables
testIndGLMMCR | Linear mixed models conditional independence test for longitudinal class variables
testIndGLMMGamma | Linear mixed models conditional independence test for longitudinal class variables
testIndGLMMLogistic | Linear mixed models conditional independence test for longitudinal class variables
testIndGLMMNormLog | Linear mixed models conditional independence test for longitudinal class variables
testIndGLMMOrdinal | Linear mixed models conditional independence test for longitudinal class variables
testIndGLMMPois | Linear mixed models conditional independence test for longitudinal class variables
testIndGLMMReg | Linear mixed models conditional independence test for longitudinal class variables
testIndIGreg | Regression conditional independence test for positive response variables.
testIndLMM | Linear mixed models conditional independence test for longitudinal class variables
testIndLogistic | Conditional independence test for binary, categorical or ordinal class variables
testIndMMFisher | Fisher and Spearman conditional independence test for continuous class variables
testIndMMReg | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
testIndMultinom | Conditional independence test for binary, categorical or ordinal class variables
testIndMVreg | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
testIndNB | Regression conditional independence test for discrete (counts) class dependent variables
testIndNormLog | Regression conditional independence test for positive response variables.
testIndOrdinal | Conditional independence test for binary, categorical or ordinal class variables
testIndPois | Regression conditional independence test for discrete (counts) class dependent variables
testIndQBinom | Conditional independence test for binary, categorical or ordinal class variables
testIndQPois | Regression conditional independence test for discrete (counts) class dependent variables
testIndReg | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
testIndRQ | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
testIndSpearman | Fisher and Spearman conditional independence test for continuous class variables
testIndTimeLogistic | Conditional independence test for the static-longitudinal scenario
testIndTimeMultinom | Conditional independence test for the static-longitudinal scenario
testIndTobit | Conditional independence test for survival data
testIndZIP | Regression conditional independence test for discrete (counts) class dependent variables
topological_sort | Topological sort of a DAG
transitiveClosure | Returns the transitive closure of an adjacency matrix
triangles.search | Search for triangles in an undirected graph

## -- U --

undir.path | Undirected path(s) between two nodes
univariateScore | Internal MXM Functions
univariateScore.gee | Internal MXM Functions
univariateScore.glmm | Internal MXM Functions
univariateScore.ma | Internal MXM Functions
univariateScore.timeclass | Internal MXM Functions
univregs | Univariate regression based tests

## -- W --

wald.betaregs | Many simple beta regressions.
wald.Internalmmpc | Internal MXM Functions
wald.Internalses | Internal MXM Functions
wald.logisticregs | Many Wald based tests for logistic and Poisson regressions with continuous predictors
wald.mmpc | SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets
wald.poissonregs | Many Wald based tests for logistic and Poisson regressions with continuous predictors
wald.ses | SES: Feature selection algorithm for identifying multiple minimal, statistically-equivalent and equally-predictive feature signatures MMPC: Feature selection algorithm for identifying minimal feature subsets
wald.univariateScore | Internal MXM Functions
wald.univregs | Univariate regression based tests
wald.zipregs | Many simple zero inflated Poisson regressions.
waldBeta | Beta regression conditional independence test for proportions/percentage class dependent variables and mixed predictors
waldBinom | Binomial regression conditional independence test for success rates (binomial)
waldCR | Conditional independence test for survival data
waldER | Conditional independence test for survival data
waldGamma | Regression conditional independence test for positive response variables.
waldIGreg | Regression conditional independence test for positive response variables.
waldLogistic | Conditional independence test for binary, categorical or ordinal class variables
waldmmpc.model | Regression model(s) obtained from SES or MMPC
waldmmpc.path | MMPC solution paths for many combinations of hyper-parameters
waldMMReg | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables
waldNB | Regression conditional independence test for discrete (counts) class dependent variables
waldNormLog | Regression conditional independence test for positive response variables.
waldOrdinal | Conditional independence test for binary, categorical or ordinal class variables
waldPois | Regression conditional independence test for discrete (counts) class dependent variables
waldQBinom | Conditional independence test for binary, categorical or ordinal class variables
waldses.model | Regression model(s) obtained from SES or MMPC
waldTobit | Conditional independence test for survival data
waldWR | Conditional independence test for survival data
waldZIP | Regression conditional independence test for discrete (counts) class dependent variables
weibreg.mxm | Cross-Validation for SES and MMPC
wr.fsreg | Internal MXM Functions
wr.fsreg_2 | Internal MXM Functions

## -- Z --

zip.bsreg | Internal MXM Functions
zip.fsreg | Internal MXM Functions
zip.fsreg_2 | Internal MXM Functions
zip.mod | Zero inflated Poisson regression
zip.reg | Zero inflated Poisson regression
zip.regs | Many simple zero inflated Poisson regressions.
zipmle.wei | Internal MXM Functions
zipwei | Internal MXM Functions