Nothing
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()
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