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. Additionally, the package includes two algorithms for constructing the skeleton of a Bayesian network. |
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 |
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.reg | Internal MXM Functions |
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.llr.fsreg | Internal MXM Functions |
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 | Generic orthogonal matching pursuit(gOMP) for big data |
big.gomp.path | Generic 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 |
boot.gomp | Generic orthogonal matching pursuit (gOMP) |
bs.g2 | Internal MXM Functions |
bs.reg | Variable selection in regression models with backward selection |
bsreg.big | Internal MXM Functions |
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 |
censIndLLR | 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.fbed.lmm.reg | Cross-validation of the FBED with LMM |
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 |
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 |
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.llr.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.spml.bsreg | Internal MXM Functions |
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 |
exporeg.mxm | Cross-Validation for SES and MMPC |
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.nb | Internal MXM Functions |
fbed.glmm.nb.reps | 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 |
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.bsreg2 | 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.nb.bsreg | Internal MXM Functions |
glmm.nb.reps.bsreg | Internal MXM Functions |
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 | Generic orthogonal matching pursuit (gOMP) |
gomp.path | Generic 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 |
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 |
kfbed.gee.reg | Internal MXM Functions |
kfbed.glmm.reg | Internal MXM Functions |
kfbed.reg | Internal MXM Functions |
llr.bsreg | Internal MXM Functions |
llrreg.mxm | Cross-Validation for SES and MMPC |
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 |
logiquant.regs | Many simple quantile regressions using logistic regressions. |
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 |
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.fsreg | Variable selection in generalised linear regression models with forward selection |
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 |
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.sel | Variable selection using the PC-simple 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.mmpc.path | MMPC solution paths for many combinations of hyper-parameters |
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. |
permLLR | Conditional independence test for survival data |
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 |
prec.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 |
quasibinom.fsreg | Internal MXM Functions |
quasibinom.fsreg_2 | Internal MXM Functions |
quasipois.fsreg | Internal MXM Functions |
quasipois.fsreg_2 | Internal MXM Functions |
R0 | Internal MXM Functions |
R1 | Internal MXM Functions |
R2 | Internal MXM Functions |
R3 | Internal MXM Functions |
rdag | Data simulation from a DAG. |
rdag2 | Data simulation from a DAG. |
read.big.data | Read big data or a big.matrix object |
reg.fit | Regression modelling |
regbeta | Internal MXM Functions |
regbetawei | Internal MXM Functions |
regzinb | 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 | Data simulation from a DAG. |
rq.mxm | Cross-Validation for SES and MMPC |
score.univregs | Univariate regression based tests |
sens.mxm | Cross-Validation for SES and MMPC |
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 |
sp.logiregs | Many approximate simple logistic regressions. |
spec.mxm | Cross-Validation for SES and MMPC |
spml.bsreg | Internal MXM Functions |
supervised.pca | Supervised PCA |
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 |
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 |
testIndGLMMNB | 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 |
testIndSPML | Circular regression conditional independence test for circular class dependent variables and continuous predictors. |
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 |
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 |
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.mmpc.path | MMPC solution paths for many combinations of hyper-parameters |
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. |
waldLLR | Conditional independence test for survival data |
waldLogistic | Conditional independence test for binary, categorical or ordinal class variables |
waldmmpc.model | Regression model(s) obtained from SES or MMPC |
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 |
zinb.mle | Internal MXM Functions |
zinb.mod | Zero inflated Poisson and negative binomial regression |
zinb.reg | Zero inflated Poisson and negative binomial regression |
zip.bsreg | Internal MXM Functions |
zip.fsreg | Internal MXM Functions |
zip.fsreg_2 | Internal MXM Functions |
zip.mod | Zero inflated Poisson and negative binomial regression |
zip.reg | Zero inflated Poisson and negative binomial regression |
zip.regs | Many simple zero inflated Poisson regressions. |
zipmle.wei | Internal MXM Functions |
zipwei | Internal MXM Functions |