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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.
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