- Various further fixes to
`MoE_stepwise`

:- Added the arg.
`fullMoE`

(defaulting to`FALSE`

) which allows restricting the search to “full”

MoE models where the same set of covariates appears in both the gating & expert networks. - When
`initialModel`

/`initialG`

is given, the`"all"`

option for`noise.gate`

&`equalPro`

now reverts to`"both"`

whenever`"all"`

would unnecessarily duplicate candidate models. - Small speed-up if
`gating`

&/or`expert`

have covariates that are already in`initialModel`

. - Small speed-up by searching
`G=1`

`equalPro`

models w/ expert covariates only once. - Two fixes to handle how
`initialModel`

and`modelNames`

interact:- It’s now assumed (else warned) that
`initialModel`

should be optimal w.r.t. model type. - The supplied
`modelNames`

are augmented with`initialModel$modelName`

if needs be.

- It’s now assumed (else warned) that

- Added the arg.
`MoE_control`

gains the arg.`exp.init$estart`

so the paper’s Algorithm 1 can work as intended:

`exp.init$estart`

toggles the behaviour of`init.z="random"`

in the presence of expert covariates

when`exp.init$mahalanobis=TRUE`

&`nstarts > 1`

: when`FALSE`

(the default/old behaviour), all

random starts are put through the initial reallocation routine and then subjected to full runs of the EM;

when`TRUE`

, only the single best random start obtained from this routine is subjected to the full EM.- Handled name mismatches for optional args. w/
`list(...)`

defaults in`MoE_control`

/`MoE_gpairs`

. - Fixed printing of
`noise.gate`

in`MoE_compare`

for`G=1`

models w/ noise & gating covariates. - Improved checks on
`G`

in`MoE_clust`

.

- Various edits to
`MoE_stepwise()`

(thanks, in part, to requests from Dr. Konstantinos Perrakis):- Added
`initialModel`

arg. for specifying an initial model from which to begin the search,

which may already be a mixture and may already include covariates, etc. - Added
`initialG`

arg. as a simpler alternative when the only available

prior information is on the number of components. - Added
`stepG`

arg. (defaults to`TRUE`

) for fixing the number of components

& searching only over different covariate configurations (i.e. when`FALSE`

). - Speedups by preventing superfluous searches for equal

mixing proportion models when there are gating covariates. `noise.gate`

arg. now also invoked when adding components to models with gating covariates

& a noise component (previously only when adding gating covariates to models with noise).`equalPro`

&`noise.gate`

args. gain new default`"all"`

(see documentation for details).- Stronger checks on
`network.data`

argument.

- Added
- New methods and edits related to prediction:
- Added
`fitted`

method for`"MoEClust"`

objects (a wrapper to`predict.MoEClust`

). - Added
`predict`

,`fitted`

, &`residuals`

methods for`"MoE_gating"`

objects, i.e.`x$gating`

. - Added
`predict`

,`fitted`

, &`residuals`

methods for`"MoE_expert"`

objects, i.e.`x$expert`

. - Minor edits to
`predict.MoEClust`

for models without expert network covariates. - Minor fixes to returned
`x$gating`

object for`equalPro=TRUE`

models with a noise component.

- Added
- Various edits & documentation improvements to
`MoE_gpairs`

:- Fixes to ellipses for models with expert covariates due to fix to
`expert_covar`

(see below). `mosaic.pars`

gains logical arg.`mfill=TRUE`

, to toggle between filling select tiles with colour

(new default behaviour), or outlining select tiles with colour (old behaviour).`boxplot.pars`

arg. added to allow customising boxplot and violin plot panels,

with related fixes to colourisation in upper-triangular panels.- Fixes re:
`scatter.pars$eci.col`

: now governs colours of ellipses*and*regression lines. `scatter.pars$uncert.pch`

added; now plotting symbols in covariate-related scatterplots

are only modified in`response.type="uncertainty"`

plots when`uncert.cov`

is`TRUE`

.- Fixes to axis labels for diagonal panels involving factors.
- Various colour-related args. now inherit sensible defaults if scatterplot colours are specified.

- Fixes to ellipses for models with expert covariates due to fix to
`expert_covar`

gains the arg.`weighted`

to ensure cluster membership probabilities are properly

accounted for in estimating the extra variability due to the component means: defaults to`TRUE`

,

but`weighted=FALSE`

is provided as an option for recovering the old (not recommended) behaviour.- A warning message is now printed if the MLR in the gating network ever fails to converge,

prompting the user to modify the`itmax`

arg. to`MoE_control`

: the 3^{rd}element of this arg. governs

the maximum number of MLR iterations — consequently, its default has been modified from`100`

to

`1000`

(thanks to a prompt from Dr. Georgios Karagiannis), which has the effect of slowing down

internal calls to`nnet::multinom`

but generally reduces the required number of EM iterations. - Minor fix to
`MoE_compare`

whenever the optimal model needs to be refitted. - Fixed conflict between
`mclust::as.Mclust`

&`MoEClust::as.Mclust`

:

`as.Mclust.MoEClust`

now works regardless of order in which`mclust`

&`MoEClust`

are loaded. - Stronger checks for variables in
`gating`

&`expert`

formulas which are not found in`network.data`

. - Minor speed-up to initialisation for univariate response data with expert network covariates.
- Minor speed-ups to some other utility functions.
- Minor documentation, vignette, and vignette styling edits.

- Minor
`MoE_stepwise`

speed-ups by avoiding duplication of initialisation for certain steps. - Minor fix to
`MoE_stepwise`

for univariate data sets without covariates. - Prettier axis labels for
`MoE_uncertainty`

plots. - Minor CRAN compliance edits to the vignette.

- New
`MoE_control`

arg.`posidens=TRUE`

ensures code no longer crashes when observations

have positive log-density: previous behaviour is recoverable by setting`posidens=FALSE`

. `MoE_control`

gains the arg.`asMclust`

(`FALSE`

, by default) which modifies the

`stopping`

and`hcUse`

arguments such that`MoEClust`

and`mclust`

behave similarly

for models*with no covariates in either network*(thanks to a request from Prof. Kamel Gana).- Fixes to plotting colours & symbols in
`MoE_gpairs`

(thanks to Dr. Natasha De Manincor):- Corrected mosaic panels (colours).
- Accounted for empty clusters in all panels (colours & symbols).

- Fixed bug in
`predict.MoEClust`

when no`newdata`

is supplied to models with no gating covariates. `MoE_clust`

&`MoE_stepwise`

now coerce`"character"`

covariates to`"factor"`

(for later plotting).- Further improvements to
`summary`

method for`MoE_expert`

objects. - Fixes to
`print`

&`summary`

methods for`MoE_gating`

objects if`G=1`

or`equalPro=TRUE`

. - Additional minor edits to
`MoE_plotGate`

. `print.MoECompare`

gains the args.`maxi`

,`posidens=TRUE`

, &`rerank=FALSE`

.- Ensured
`lattice(>=0.12)`

,`matrixStats(>=0.53.1)`

, &`mclust(>=5.4)`

in`Imports:`

. - Ensured
`clustMD(>=1.2.1)`

and`geometry(>=0.4.0)`

in`Suggests:`

. - Use of
`NCOL`

/`NROW`

where appropriate. - Package startup message now checks if newer version of package is available from CRAN.
- Updated citation info after publication in
*Advances in Data Analysis and Classification*. - Updated maintainer e-mail address.
- Minor documentation, examples, and CRAN compliance +
`mclust`

compatibility edits.

- Maintenance release for compatibility with R 4.0.0 - minor edits.
`summary.MoEClust`

gains the printing-related arguments`classification=TRUE`

,

`parameters=FALSE`

, and`networks=FALSE`

(thanks to a request from Prof. Kamel Gana).- Related improvements to
`print`

/`summary`

methods for`MoE_gating`

&`MoE_expert`

objects. - Minor speed-up for
`G=1`

models with expert network covariates. - Improvements to
`MoE_plotGate`

, with new`type`

,`pch`

, and`xlab`

defaults. - Added informative
`dimnames`

to returned`parameters`

from`MoE_clust()`

. - Documentation, vignette, examples, and references improvements.

- Various fixes and improvements to initialisation when there are expert network covariates:
`MoE_mahala`

now correctly uses the covariance of`resids`

rather than the response.- New
`MoE_mahala`

arg.`identity`

allow use of Euclidean distance instead:

this argument can also be passed via`exp.init$identity`

to`MoE_control`

. - Convergence of the initialisation procedure now explicitly monitored & sped-up.
- Values of the criterion being minimised are now returned as an attribute.
- The number of iterations of the initialisation algorithm are also returned as an attribute.
`MoE_control`

arg.`exp.init$max.init`

now defaults to`.Machine$integer.max`

.- Improved checks on the
`resids`

arg. to`MoE_mahala`

. - Greatly expanded the
`MoE_mahala`

examples.

- Improvements to
`predict.MoEClust`

:- Now returns the predicted values of the gating and expert networks.
- Now returns the predictions from the expert network of the most probable component

(`MAPy`

), in addition to the (aggregated) predicted responses (`y`

). - New arg.
`MAPresids`

governs whether residuals are computed against`MAPy`

or`y`

. - New arg.
`use.y`

(see documentation for details). - Now properly allows empty
`newdata`

for models with no covariates of any kind. - Fixed prediction for equal mixing proportion models when
`discard.noise=FALSE`

.

- Fixed small
`MoE_stepwise`

bugs when- only one of
`gating`

or`expert`

are supplied. - univariate response
`data`

are supplied. - moving from G=1 to G=2 with equal mixing proportions and no covariates.
- discarding covariates present in the response data.

- only one of
- Odds ratios now returned (and printed) when calling
`summary`

on`x$gating`

. `noise_vol`

now returns correction location for univariate data when`reciprocal=TRUE`

.- Spell-checking of documentation and fixes to
`donttest`

examples.

- Fixed small bugs in
`MoE_stepwise`

:- Improved checks on
`network.data`

and`data`

. - Prevented
`z.list`

from being suppliable.

- Fixes when
`equalPro="yes"`

&`noise=TRUE`

. - Fixes for supplying optional
`MoE_control`

arguments (also for`MoE_clust`

). - Prevented termination if adding a component fails,

provided at least one other step doesn’t fail.

- Improved checks on
- Fixed
`discard.noise=TRUE`

behaviour for`MoE_clust`

,`predict.MoEClust`

, &

`residuals.MoEClust`

for models with a noise component fitted via`"CEM"`

. - Minor fixes to
`noise_vol`

function and handling of`noise.meth`

arg. to`MoE_control`

. - Slight speed-up to E-step/C-step for models with a noise component.
- Initial allocation matrices now stored as attributes to
`MoE_clust`

output (see`?MoE_control`

). - Anti-aliasing of vignette images.
- Updated citation info after online publication in
*Advances in Data Analysis and Classification*.

- Exported function
`MoE_stepwise`

for conducting a greedy forward stepwise

search to find the optimal model in terms of the number of components, GPCM

covariance parameterisation, and the subsets of gating/expert network covariates. `MoE_control`

&`predict.MoEClust`

gain the arg.`discard.noise`

:

Default of`FALSE`

retains old behaviour (see documentation for details).`MoE_control`

gains the arg.`z.list`

and the`init.z`

arg. gets the option`"list"`

:

this allows manually supplying (soft or hard) initial cluster allocation matrices.- New args. and small fixes added to
`MoE_gpairs`

:`uncert.cov`

arg. added to control uncertainty point-size in panels with covariates.`density.pars`

gains arg.`label.style`

.`scatter.pars`

&`stripplot.pars`

gain args.`noise.size`

&`size.noise`

.`barcode.pars$bar.col`

slightly fixed from previous update.- Colours for
`"violin"`

type plots now accurate for MAP panels.

- Slight speed-up to
`noise_vol`

when`method="ellipsoidhull"`

. - Small fix to
`predict.MoEClust`

when`resid=TRUE`

for models with expert covariates. - Small fix related to
`...`

construct for`residuals.MoEClust`

. - All printing related to noise-only models no longer shows the model name (there is none!).
- Other small fixes to
`print.MoEClust`

,`print.summary_MoEClust`

, &`print.MoECompare`

. - Cosmetic fix to returned
`gating`

objects for`equalPro=TRUE`

models. - Removed
`parallel`

package from`Suggests:`

.

`noise_vol`

now also returns the location of the centre of mass of the region

used to estimate the hypervolume, regardless of the method employed. This fixes:`predict.MoEClust`

for any models with a noise component (see below).- The summary of means for models with expert covariates and a noise component.
- The location of the MVN ellipses for such models in
`MoE_gpairs`

(see below).

- Furthermore, calculation of the hypervolume in
`noise_vol`

for data with >2 dimensions

is now correct when`method="ellipsoidhull"`

, owing to a bug in the`cluster`

package. - Other fixes and speed-ups for the
`MoE_gpairs`

plotting function:- Added arg.
`expert.covar`

(& also to`as.Mclust`

function). - Fixed location of MVN ellipses for models with noise & expert covariates (see above).
- Fixes when
`response.type="density"`

for all models with a noise component. - Speed-up when
`response.type="density"`

for models with covariates of any kind. - Fixes to labelling for models with a noise component.
- Fixed handling of
`subset$data.ind`

&`subset$cov.ind`

arguments. - Barcode type plots now have colour for panels involving the MAP classification.
- Barcode type plots now respect the arg.
`buffer`

. - Use of colour in
`MoE_plotGate`

is now consistent with`MoE_gpairs`

.

- Added arg.
- Fixes to how
`gating`

&`expert`

formulas are handled:- Allowed specification of formulas with dropped variables of the form
`~.-a-b`

. - Allowed formulas with no intercept of the form
`~c-1`

. - Allowed interaction effects, transformations and higher-order terms using
`I()`

. - Small related fixes to
`drop_levels`

&`drop_constants`

functions.

- Allowed specification of formulas with dropped variables of the form
`MoE_compare`

gains arg.`noise.vol`

for overriding the`noise.meth`

arg.:

this allows specifying an improper uniform density directly via the (hyper)volume,

& hence adding noise to models for high-dimensional data for which`noise_vol()`

fails.- Fixed bug for
`equalPro`

models with noise component, and also added`equalNoise`

arg.

to`MoE_control`

, further controlling`equalPro`

in the presence of a noise component. - Fixes to
`predict.MoEClust`

for the following special cases:- Fixes for any models with a noise component (see
`noise_vol`

comment above). - Accounted for predictions of single observations for models with a noise component.
- Accounted for models with equal mixing proportions.

- Fixes for any models with a noise component (see
- Accounted for categorical covariates in the
`x.axis`

arg. to`MoE_plotGate`

. `tau0`

can now also be supplied as a vector in the presence of gating covariates.- Fix to
`expert_covar`

for univariate models. - Slight
`MoE_estep`

speed-up due to removal of unnecessary`sweep()`

. - Small fixes for when
`clustMD`

is invoked, and added`snow`

package to`Suggests:`

. - The
`nnet`

arg.`MaxNWts`

now passable to gating network`multinom`

call via`MoE_control`

. - Improved printing of output and handling of ties, especially for
`MoE_compare`

. - Many documentation and vignette improvements.

- 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.

- Related new function
- 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`

, &`MoE_plotCrit`

accordingly.

- Behaves exactly as per
- 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. `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.

- 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"`

.

`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`

&`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 relevant 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.

`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"`

.

- 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.