MoEClust: Gaussian Parsimonious Clustering Models -

with Gating and Expert Network Covariates

MoEClust v1.2.1 - (4^th release [patch update]: 2018-12-11)

New Features, Improvements, Bug Fixes & Miscellaneous Edits

• New MoE_control arg. algo allows model fitting using the "EM" or "CEM" algorithm:
  • Related new function MoE_cstep added.
  • Extra algo option "cemEM" allows running EM starting from convergence of CEM.
• Added LOGLIK to MoE_clust output, giving maximal log-likelihood values for all fitted models.
  • Behaves exactly as per DF/ITERS, etc., with associated printing/plotting functions.
  • Edited MoE_compare, summary.MoEClust, and MoE_plotCrit accordingly.
• New MoE_control arg. nstarts allows for multiple random starts when init.z="random".
• New MoE_control arg. tau0 provides another means of initialising the noise component.
• If clustMD is invoked for initialisation, models are now run more quickly in parallel.
• Allowed gating and expert formulas without intercept terms (drop_constants also edited).
• MoE_plotGate now allows a user-specified x-axis against which mixing proportions are plotted.
• Fixed bug in checking for strictly increasing log-likelihood estimates.

MoEClust v1.2.0 - (3^rd release [minor update]: 2018-08-24)

New Features & Improvements

• New predict.MoEClust function added: predicts cluster membership probability,
  MAP classification, and fitted response, using only new covariates or new covariates &
  new response data, with noise components (and the noise.gate option) accounted for.
• New plotting function MoE_Uncertainty added (callable within plot.MoEClust):
  visualises clustering uncertainty in the form of a barplot or an ordered profile plot,
  allowing reference to be made to the true labels, or not, in both cases.
• Specifying response.type="density" to MoE_gpairs now works properly for models with
  gating &/or expert network covariates. Previous approach which evaluated the density using
  averaged gates &/or averaged means replaced by more computationally expensive but correct
  approach, which evaluates MVN density for every observation individually and then averages.
• Added clustMD package to Suggests:. New MoE_control argument exp.init$clustMD
  governs whether categorical/ordinal covariates are also incorporated into the initialisation
  when isTRUE(exp.init$joint) & clustMD is loaded (defaults to FALSE, works with noise).
• Added drop.break arg. to MoE_control for further control over the extra initialisation
  step invoked in the presence of expert covariates (see Documentation for details).
• Sped-up MoE_dens for the EEE & VVV models by using already available Cholesky factors.
• Other new MoE_control arguments:
  • km.args specifies kstarts & kiters when init.z="kmeans".
  • Consolidated args. related to init.z="hc" & noise into hc.args & noise.args.
  • hc.args now also passed to call to mclust when init.z="mclust".
  • init.crit ("bic"/"icl") controls selection of optimal mclust/clustMD
    model type to initialise with (if init.z="mclust" or isTRUE(exp.init$clustMD));
    relatedly, initialisation now sped-up when init.z="mclust".

Bug Fixes & Miscellaneous Edits

• ITERS replaces iters as the matrix of the number of EM iterations in MoE_clust output:
  • iters now gives this number for the optimal model.
    • ITERS now behaves like BIC/ICL etc. in inheriting the "MoECriterion" class.
      
    • iters now filters down to summary.MoEClust and the associated printing function.
      
    • ITERS now filters down to MoE_compare and the associated printing function.
• Fixed point-size, transparency, & plotting symbols when response.type="uncertainty"
  within MoE_gpairs to better conform to mclust: previously no transparency.
• subset arg. to MoE_gpairs now allows data.ind=0 or cov.ind=0, allowing plotting of
  response variables or plotting of the covariates to be suppressed entirely.
• Clarified MVN ellipses in MoE_gpairs plots.
• sigs arg. to MoE_dens and MoE_estep must now be a variance object, as per variance
  in the parameters list from MoE_clust & mclust output, the number of clusters G,
  variables d & modelName is inferred from this object: the arg. modelName was removed.
• MoE_clust no longer returns an error if init.z="mclust" when no gating/expert network
  covariates are supplied; instead, init.z="hc" is used to better reproduce mclust output.
• resid.data now returned by MoE_clust as a list, to better conform to MoE_dens.
• Renamed functions MoE_aitken & MoE_qclass to aitken & quant_clust, respectively.
• Rows of data w/ missing values now dropped for gating/expert covariates too (MoE_clust).
• Logical covariates in gating/expert networks now coerced to factors.
• Fixed small bug calculating linf within aitken & the associated stopping criterion.
• Final linf estimate now returned for optimal model when stopping="aitken" & G > 1.
• Removed redundant extra M-step after convergence for models without expert covariates.
• Removed redundant & erroneous resid & residuals args. to as.Mclust & MoE_gpairs.
• MoE_plotCrit, MoE_plotGate & MoE_plotLogLik now invisibly return revelant quantities.
• Corrected degrees of freedom calculation for G=0 models when noise.init is not supplied.
• Fixed drop_levels to handle alphanumeric variable names and ordinal variables.
• Fixed MoE_compare when a mix of models with and without a noise component are supplied.
• Fixed MoE_compare when optimal model has to be re-fit due to mismatched criterion.
• Fixed y-axis labelling of MoE_Uncertainty plots.
• print.MoECompare now has a digits arg. to control rounding of printed output.
• Better handling of tied model-selection criteria values in MoE_clust & MoE_compare.
• Interactions and higher-order terms are now accounted for within drop_constants.
• Replaced certain instances of is.list(x) with inherits(x, "list") for stricter checking.
• Added extra checks for invalid gating &/or expert covariates within MoE_clust.
• Added mclust::clustCombi/clustCombiOptim examples to as.Mclust documentation.
• Added extra precautions for empty clusters: during initialisation & during EM.
• Added utility function MoE_news for accessing this NEWS file.
• Added message if optimum G is at either end of the range considered.
• Tidied indentation/line-breaks for cat/message/warning calls for printing clarity.
• Added line-breaks to usage sections of multi-argument functions.
• Corrected MoEClust-package help file (formerly just MoEClust).
• Many documentation clarifications.

MoEClust v1.1.0 - (2^nd release [minor update]: 2018-02-06)

New Features & Improvements

• MoE_control gains the noise.gate argument (defaults to TRUE): when FALSE,
  the noise component’s mixing proportion isn’t influenced by gating network covariates.
• x$parameters$mean is now reported as the posterior mean of the fitted values when
  there are expert network covariates: when there are no expert covariates, the posterior
  mean of the response is reported, as before. This effects the centres of the MVN ellipses
  in response vs. response panels of MoE_gpairs plots when there are expert covariates.
• New function expert_covar used to account for variability in the means, in the presence
  of expert covariates, in order to modify shape & size of MVN ellipses in visualisations.
• MoE_control gains the hcUse argument (defaults to "VARS" as per old mclust versions).
• MoE_mahala gains the squared argument + speedup/matrix-inversion improvements.
• Speed-ups, incl. functions from matrixStats (on which MoEClust already depended).
• The MoE_gpairs argument addEllipses gains the option "both".

Bug Fixes & Miscellaneous Edits

• Fixed bug when equalPro=TRUE in the presence of a noise component when there are
  no gating covariates: now only the mixing proportions of the non-noise components
  are constrained to be equal, after accounting for the noise component.
• MoE_gpairs argument scatter.type gains the options lm2 & ci2 for further control
  over gating covariates. Fixed related bug whereby lm & ci type plots were being
  erroneously produced for panels involving pairs of continuous covariates only.
• Fixed bugs in MoE_mahala and in expert network estimation with a noise component.
• G=0 models w/ noise component only can now be fitted without having to supply noise.init.
• MoE_compare now correctly prints noise information for sub-optimal models.
• Slight edit to criterion used when stopping="relative": now conforms to mclust.
• Added check.margin=FALSE to calls to sweep().
• Added call.=FALSE to all stop() messages.
• Removed dependency on the grid library.
• Many documentation clarifications.

MoEClust v1.0.0 - (1^st release: 2017-11-28)