A B C D E F G I L M N O P R S T U V 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. |
acc.mxm | Cross-Validation for SES and MMPC |
acc_multinom.mxm | Cross-Validation for SES and MMPC |
apply_ideq | Internal MXM Functions |
apply_ideq.ma | Internal MXM Functions |
apply_ideq.temporal | Internal MXM Functions |
auc | ROC and area under the curve |
auc.mxm | Cross-Validation for SES and MMPC |
beta.bsreg | Internal MXM Functions |
beta.fsreg | Internal MXM Functions |
beta.mod | Beta regression |
beta.mxm | Cross-Validation for SES and MMPC |
beta.reg | Beta regression |
beta.regs | Many simple beta regressions. |
betamle.wei | Internal MXM Functions |
bic.betafsreg | Internal MXM Functions |
bic.fsreg | Variable selection in regression models with forward selection using BIC |
bic.glm.fsreg | Variable selection in generalised linear models with forward selection based on BIC |
bic.zipfsreg | Internal MXM Functions |
bs.reg | Variable selection in regression models with backward selection |
cat.ci | Internal MXM Functions |
censIndCR | Conditional independence test for survival data |
censIndER | Conditional independence test for survival data |
censIndWR | Conditional independence test for survival data |
ci.mxm | Cross-Validation for SES and MMPC |
ciwr.mxm | Cross-Validation for SES and MMPC |
compare_p_values | Internal MXM Functions |
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 |
coxph.mxm | Cross-Validation for SES and MMPC |
cv.mmpc | Cross-Validation for SES and MMPC |
cv.ses | Cross-Validation for SES and MMPC |
cvmmpc.par | Internal MXM Functions |
cvses.par | Internal MXM Functions |
dag2eg | Transforms a DAG into an essential graph |
dag_to_eg | Internal MXM Functions |
dist.condi | Conditional independence test for continuous class variables with and without permutation based p-value |
equivdags | Check Markov equivalence of two DAGs |
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 |
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 |
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.glmbs | Internal MXM Functions |
iamb.zipbs | Internal MXM Functions |
IdentifyEquivalence | Internal MXM Functions |
IdentifyEquivalence.ma | Internal MXM Functions |
IdentifyEquivalence.temporal | Internal MXM Functions |
identifyTheEquivalent | Internal MXM Functions |
identifyTheEquivalent.ma | Internal MXM Functions |
identifyTheEquivalent.temporal | Internal MXM Functions |
internaliamb.binombs | Internal MXM Functions |
internaliamb.lmbs | Internal MXM Functions |
internaliamb.poisbs | Internal MXM Functions |
Internalmammpc | Internal MXM Functions |
Internalmases | Internal MXM Functions |
InternalMMPC | Internal MXM Functions |
InternalMMPC.temporal | Internal MXM Functions |
InternalSES | Internal MXM Functions |
InternalSES.temporal | Internal MXM Functions |
is.sepset | Internal MXM Functions |
lm.fsreg | Variable selection in linear regression models with forward selection |
lm.fsreg_2 | Internal MXM Functions |
lm.fsreg_2.heavy | Internal MXM Functions |
lm.fsreg_heavy | Variable selection in linear regression models with forward selection |
lm.mxm | Cross-Validation for SES and MMPC |
lmrob.mxm | Cross-Validation for SES and MMPC |
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 |
mammpc.output | Class '"mammpc.output"' |
mammpc.output-class | Class '"mammpc.output"' |
mases.output | Class '"mases.output"' |
mases.output-class | Class '"mases.output"' |
max_min_assoc | Internal MXM Functions |
max_min_assoc.ma | Internal MXM Functions |
max_min_assoc.temporal | Internal MXM Functions |
mb | Returns and plots, if asked, the Markov blanket of a node (or variable) |
min_assoc | Internal MXM Functions |
min_assoc.ma | Internal MXM Functions |
min_assoc.temporal | 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.model | Regression model(s) obtained from SES or MMPC |
mmpc.path | MMPC solution paths for many combinations of hyper-parameters |
MMPC.temporal | 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 |
MMPC.temporal.output | Class '"MMPC.temporal.output"' |
MMPC.temporal.output-class | Class '"MMPC.temporal.output"' |
MMPCoutput | Class '"MMPCoutput"' |
MMPCoutput-class | Class '"MMPCoutput"' |
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 and plots, if asked, the node(s) and their neighbour(s), if there are any. |
ordinal.mxm | Cross-Validation for SES and MMPC |
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.skel | The skeleton of a Bayesian network produced by the PC algorithm |
permcor | Permutation based p-value for the Pearson correlation coefficient |
permcorrels | Permutation based p-value for the Pearson correlation coefficient |
permFisher | Fisher and Spearman conditional independence test for continuous class variables |
plot-method | Class '"MMPC.temporal.output"' |
plot-method | Class '"MMPCoutput"' |
plot-method | Class '"SES.temporal.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 |
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) |
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 |
rq.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.model | Regression model(s) obtained from SES or MMPC |
SES.temporal | 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 |
SES.temporal.output | Class '"SES.temporal.output"' |
SES.temporal.output-class | Class '"SES.temporal.output"' |
SESoutput | Class '"SESoutput"' |
SESoutput-class | Class '"SESoutput"' |
summary-method | Class '"MMPC.temporal.output"' |
summary-method | Class '"MMPCoutput"' |
summary-method | Class '"SES.temporal.output"' |
summary-method | Class '"SESoutput"' |
summary-method | Class '"mammpc.output"' |
summary-method | Class '"mases.output"' |
tc.plot | Plot of longitudinal data |
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 |
testIndGLMM | Linear mixed models conditional independence test for longitudinal class variables |
testIndIGreg | Linear (and non-linear) regression conditional independence test for continous univariate and multivariate response variables |
testIndLogistic | 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 |
testIndPois | 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 |
testIndSpeedglm | Conditional independence test for continuous, binary and discrete (counts) variables with thousands of observations |
testIndZIP | Regression conditional independence test for discrete (counts) class dependent variables |
topological_sort | Internal MXM Functions |
transitiveClosure | Returns the transitive closure of an adjacency matrix |
undir.path | Undirected path(s) between two nodes |
univariateScore | Internal MXM Functions |
univariateScore.ma | Internal MXM Functions |
univariateScore.temporal | Internal MXM Functions |
univregs | Univariate regression based tests |
vara | Internal MXM Functions |
weibreg.mxm | Cross-Validation for SES and MMPC |
zip.bsreg | Internal MXM Functions |
zip.fsreg | 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 |