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sim() when EVV, EVE, and VVE models for G =
1.summary.MclustBootstrap() when computing
confidence intervals for G = 1.logsumexp() and softmax() functions
as a wrapper to efficiently implementations written in Fortran
code.me.weighted() to use convergence criterion
as in other mclust functions and improved efficiency by using the above
mentioned Fortran-based functions. This also brings computational
improvements in the weighted likelihood bootstrap implemented in
MclustBootstrap(..., type = "wlbs").MclustDA() when number of obs is less than
number of vars.MclustBootstrap(object, ..., type = "pb")
when object is of class densityMclust.MclustSSC() for components of
unlabeled data via k-means.summary.MclustSCC() for components
of unlabeled data.summary.crimcoords() method and removed argument
plot from crimcoords() function call.diabetes,
wdbc, and thyroid.cvMclustDA() uses
formula for the weighted standard deviation with weights given by folds
size.crimcoords() to compute discriminant coordinates
or crimcoords.cvMclustDA().densityMclust() by default draw a graph of the density
estimate.hc() when called to
perform agglomerative hierarchical clustering instead of using for EM
initialization.mclust.options("hcModelName") now returns
only the model to be used.partition argument of hc()
function by adding dupPartion() to remove data
duplicates.mclustBootstrapLRT() to stop if an
invalid modelName is provided or a one-component mixture
model is provided.cvMclustDA() by including
as cross-validated metrics both the classification error and the Brier
score.MclustSSC() function (and related
print, summary, plot, and
predict, methods) for semi-supervised classification.cex argument to clPairs() to control
character expansion used in plotting symbols.em() and me() have now data
as first argument.hcCriterion().CEX argument in functions with standard base
graph cex argument.ylim argument in function; it can be passed via
....icl criterion to object returned by
Mclust().quantileMclust() uses bisection line search method for
numerically computing quantiles.classPriorProbs() to estimate prior class
probabilities.BrierScore() to compute the Brier score for
assessing the accuracy of probabilistic predictions.randomOrthogonalMatrix() to generate random
orthogonal basis matrices.summary.MclustDA() internals to
provide both the classification error and the Brier score for training
and/or test data.plot.MclustDA() internals.dmvnorm() for computing the density of a general
multivariate Gaussian distribution via efficient Fortran code.NCOL() works both
for n-values vector or nx1 matrix.hcPairs are provided in the
initialization argument of mclustBIC() (and
relatives) and the number of observations exceed the threshold for
subsetting.type = "level" to type = "hdr",
and level.prob to prob, in
surfacePlot() for getting HDRs graphstype = "hdr" plot on
surfacePlot().as.Mclust().summary.MclustDA() when
modelType = "EDDA" and in general for a more compact
output.mclustBICupdate() to merge the best values from
two BIC results as returned by mclustBIC().mclustLoglik() to compute the maximal
log-likelihood values from BIC results as returned by
mclustBIC().type = "level" to
plot.densityMclust() and surfacePlot() to draw
highest density regions.meXXI() and meXXX() to exported
functions.type = "pb") in
MclustBootstrap().summary.MclustBootstrap() and to plot resampling-based
confidence intervals in plot.MclustBootstrap().catwrap() for wrapping printed lines at
getOption("width") when using cat().mclust.options() now modify the variable
.mclust in the namespace of the package, so it should work
even inside an mclust-function call.covw() when
normalize = TRUE.estepVEV() and estepVEE()
when parameters contains Vinv.plotDensityMclustd() when drawing
marginal axes.summary.MclustDA() when computing
classification error in the extreme case of a minor class of
assignment.mclustBIC() when a
noise component is present for 1-dimensional data.clustCombi()
and related functions.mclust.options(hcUse = "VARS") For more details see
help("mclust.options").subset parameter in mclust.options()
to control the maximal sample size to be used in the initial model-based
hierarchical phase.predict.densityMclust() can optionally returns the
density on a logarithm scale.packageStartupMessage().MclustBootstrap() in the
univariate data case.citation() and man pages.gmmhd() function and relative methods.MclustDRsubsel() function and relative
methods.plot.clustCombi() presents a menu in interactive
sessions, no more need of data for classification plots but extract the
data from the clustCombi object.combiTree() plot for clustCombi
objects.clPairs() now produces a single scatterplot in the
bivariate case.imputeData() when seed is provided. Now
if a seed is provided the data matrix is reproducible.imputeData() and imputePairs() some
name of arguments have been modified to be coherent with the rest of the
package.matchCluster() and
majorityVote().clustCombi
class objects.clustCombiOptim().randomPairs() when nrow of input data is
odd.plotDensityMclust2(),
plotDensityMclustd() and surfacePlot() when a
noise component is present..Fortran()
calls.structure(NULL, *) with
structure(list(), *)x to Mclust() to use BIC
values from previous computations to avoid recomputing for the same
models. The same argument and functionality was already available in
mclustBIC().x to mclustICL() to use ICL
values from previous computations to avoid recomputing for the same
models.plot.MclustBootstrap() for the
"mean" and "var" in the univariate case.as.Mclust() and
as.densityMclust() to convert object to specific mclust
classes.qclass() when
the scale of x is (very) large by making the tolerance eps
scale dependent.mclustaddson.f.predict.Mclust() and
predict.MclustDR() by implementing a more efficient and
accurate algorithm for computing the densities.Mclust()
call via summaryMclustBIC().MclustBootstrap() for using weighted
likelihood bootstrap.MclustBootstrap objects.errorBars() function.clPairsLegend() function.covw() function.hc objects.mclustBootstrapLRT() function (and corresponding
print and plot methods) for selecting the number of mixture components
based on the sequential bootstrap likelihood ratio test.MclustBootstrap() function (and corresponding
print and summary methods) for performing bootstrap inference. This
provides standard errors for parameters and confidence intervals."A quick tour of mclust" vignette as html
generated using rmarkdown and knitr. Older vignettes are included as
other documentation for the package.mvn2plot() to control colour,
lty, lwd, and pch of ellipses and mean point.emX(), emXII(),
emXXI(), emXXX(), cdensX(),
cdensXII(), cdensXXI(), and
cdensXXX(), to deal with single-component cases, so calling
the em function works even if G = 1.icl(), now it is a generic method,
with specialized methods for Mclust and
MclustDA objects.hc() (and all the
functions calling it).CITATION file upon request of
CRAN maintainers.quantileMclust() for computing the quantiles from
a univariate Gaussian mixture distribution.summaryMclustBIC(),
summaryMclustBICn(), Mclust() to return a
matrix of 1s on a single column for z even in the case of
G = 1. This is to avoid error on some plots.inst/doc with corresponding index.html.logLik.MclustDA() in the univariate
case."what" to
predict.densityMclust() function for choosing what to
retrieve, the mixture density or component density.hc() function has an additional parameter to control if
the original variables or a transformation of them should be used for
hierarchical clustering."hcUse" argument in mclust.options()
to be passed as default to hc().hypvol to Mclust object
which provide the hypervolume of the noise component when required,
otherwise is set to NA.summary.Mclust(),
print.summary.Mclust(), plot.Mclust() and
icl() in the case of presence of a noise component.plot.MclustDR() which
requires plot.new() before calling
plot.window().MclustDR() for the one-dimensional
case.Mclust man page.sim*() functions when no obs are assigned
to a component.MclustDA() allows to fit a single class model.summary.Mclust() when a subset is used for
initialization.qclass() when ties are
present in quantiles, so it always return the required number of
classes.icl() function for computing the integrated
complete-data likelihood.mclustICL() function with associated print and
plot methods.print.mclustBIC() shows also the top models based on
BIC.summary.Mclust() to return also the icl.adjustedRandIndex() function. This version
is more efficient for large vectors.adjustedRandIndex().MclustDR() and its summary
method.plot.MclustDR(..., what = "contour").plot.MclustDR(..., what = "boundaries").qclass() for selecting initial
values in case of 1D data when successive quantiles coincide.Mclust().densityMclust().MclustDA() function and methods.MclustDR() function and methods.me.weighted() function.summary.Mclust().clustCombi() and related functions (code and doc
provided by Jean-Patrick Baudry).NAMESPACE.hypvol() function to avoid overflow.hypvol() help file value description.z
component).EEE
model (hcEEE).Mclust and summary.mclustBIC help files.densityMclust() function.mclustBIC().mclustModel help
file.defaultPrior help file.mclustOptions help fileplot.mclustBIC() and plot.Mclust()
to handle modelNames.eigen() and the literatureunmap() function to optionally include
missing groups."errors" option for
randProj()."noise" option.Mclust() to handle sampling in data expression in
call.EXPR = to all switch functions that didn’t
already have it.pro component to parameters in
dens() help file.sim*() functions.Mclust() and mclustBIC() fixed to work
with G=1These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.