Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020)

The IMIFA package provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametric model-based clustering of high-dimensional data, introduced by Murphy et al. (2017) <arXiv:1701.07010v4>. The IMIFA model assumes factor analytic covariance structures within mixture components and simultaneously achieves dimension reduction and clustering without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.

The package also contains three data sets: `olive`

, `USPSdigits`

, and `coffee`

.

You can install the latest stable official release of the `IMIFA`

package from CRAN:

```
install.packages("IMIFA")
```

or the development version from GitHub:

```
# If required install devtools:
# install.packages('devtools')
devtools::install_github('Keefe-Murphy/IMIFA')
```

In either case, you can then explore the package with:

```
library(IMIFA)
help(mcmc_IMIFA) # Help on the main modelling function
```

Generally, `mcmc_IMIFA()`

is used for running the model and creating a raw results object, on which `get_IMIFA_results()`

is then called to prepare these results for posterior inference. The output of the second call be visualised in many ways using `plot.Results_IMIFA()`

.

For a more thorough intro, the vignette document is available as follows:

```
vignette("IMIFA", package="IMIFA")
```

However, if the package is installed from GitHub the vignette is not automatically created. It can be accessed when installing from GitHub with the code:

```
devtools::install_github('Keefe-Murphy/IMIFA', build_vignettes = TRUE)
```

Alternatively, the vignette is available on the package's CRAN page.

Murphy, K., Gormley, I. C. and Viroli, C. (2018) Infinite Mixtures of Infinite Factor Analysers. *To appear*. <arXiv:1701.07010v4>

`mgpControl`

gains the arguments`cluster.shrink`

and`sigma.hyper`

:`cluster.shrink`

governs invocation of cluster shrinkage MGP hyperparameter for MIFA/OMIFA/IMIFA methods.`sigma.hyper`

controls the gamma hyperprior on this parameter. The posterior mean is reported, where applicable.- Full conditionals for loadings and local/column shrinkage MGP hyperparameters edited accordingly.

- Allowed the Dirichlet concentration parameter
`alpha`

to be learned via MH steps for the OM(I)FA models.- Also allowed diminishing adaptation to tune the log-normal proposal to achieve a target acceptance rate.
- Thus
`bnpControl`

args.`learn.alpha`

,`alpha.hyper`

,`zeta`

, &`tune.zeta`

become relevant for OM(I)FA models.

- New posterior predictive model checking approach added to
`get_IMIFA_results`

(with associated plots):

Posterior Predictive Reconstruction Error (PPRE) compares bin counts of the original data with corresponding

counts for replicate draws from the posterior predictive distribution using a standardised Frobenius norm. - Added new function
`scores_MAP`

to decompose factor scores summaries

from`get_IMIFA_resuls`

into submatrices corresponding to the MAP partition. - Added new wrapper function
`sim_IMIFA_model`

to call`sim_IMIFA_data`

using

the estimated parameters from fitted`Results_IMIFA`

objects. - New
`get_IMIFA_results`

arg.`vari.rot`

allows loadings templates to be varimax rotated,

prior to Procrustes rotation, for more interpretable solutions (defaults to`FALSE`

). - New
`plot.Results_IMIFA`

argument`common`

governing`plot.meth="means"`

plots (details in documentation).

- New hyperparameter/argument defaults:
`sigma.mu`

defaults to`1`

s.t. the hypercovariance is the identity for the prior on the means;

old behaviour (using the diagonal of the sample covariance matrix) recoverable by specifying`sigma.mu=NULL`

.`prec.mu`

defaults to`0.01`

s.t. the prior on the cluster means is flat by default.`learn.d`

defaults to`TRUE`

s.t. a PYP prior is assumed for IM(I)FA models by default.`alpha.hyper`

now has a larger hyper-rate by default, to better encourage clustering.`alpha.d1`

&`alpha.d2`

now set to`2.1`

/`3.1`

rather than`2`

/`6`

to discourage exponentially fast shrinkage.`z.init`

now defaults to`"hc"`

: model-based agglomerative hierarchical clustering.

- Overhauled
`psi_hyper`

(details in documentation) for:`N <= P`

data where the sample covariance matrix is not invertible.`type="isotropic"`

uniquenesses.

- Args.
`scores`

&`loadings`

can now be supplied to`sim_IMIFA_data`

directly;

new arg.`non.zero`

controls the # effective factors (per column & cluster) when`loadings`

are instead simulated. - Sped-up 2
^{nd}label-switching move for IM(I)FA models (accounting for empty clusters). - Args. for
`hc`

can now be passed when`init.z="mclust"`

also

(previously only`"hc"`

), thus controlling how`Mclust`

is itself initialised. - Allowed
`criterion`

to be passed via`...`

in`mixfaControl`

to choose between

`mclustBIC`

/`mclustICL`

to determine optimum model to initialise with when

`z.init="mclust"`

& also sped-up`mclust`

initialisation in the process. - Added
`stop.AGS`

arg. to`mgpControl`

: renamed`adapt.at`

to`start.AGS`

for consistency. - Added
`start.zeta`

&`stop.zeta`

options to`tune.zeta`

argument in`bnpControl`

. - Allowed user-supplied
`breaks`

in the plotting functions`mat2cols`

&`heat_legend`

. - Initial cluster sizes are now shown in order to alert users to potentially bad starting values.
- Added utility function
`pareto_scale()`

.

- Fixed factor
*scores*& error metrics issues in`get_IMIFA_results`

for clustering methods:- Fixed storage of scores for infinite factor methods - now corresponds to samples where the

largest cluster-specific number of factors is`>=`

the max of the modal estimates of the same

(previously samples where**any**cluster has`>=`

the corresponding modal estimate were used):

thus, valid samples for computing error metrics also fixed and Procrustes rotation also sped-up. - Other Procrustes rotation fixes to account for label-switching.
- Other Procrustes rotation fixes specific to the IMFA/OMFA methods.

- Fixed storage of scores for infinite factor methods - now corresponds to samples where the
`range.G`

and`trunc.G`

defaults fixed, especially for small sample size settings.- Slight label-switching fixes when
`zlabels`

are supplied to`get_IMIFA_results`

;

posterior confusion matrix, cluster sizes vector, and the sampled labels themselves effected. - Prevented unnecessary Procrustes rotation for single-factor components, thus fixing some bugs.
- Fixed initialisation of uniquenesses to account for all four settings of
`uni.type`

. - Allowed conditioning on iterations with all components populated for M(I)FA models in
`get_IMIFA_results`

. - Accounted for 1-component IM(I)FA/OM(I)FA models in
`get_IMIFA_results`

. - Fixed handling of empty components when simulating cluster labels from priors in
`mcmc_IMIFA`

&`sim_IMIFA_data`

. - Ensured no. of factors
`Q`

cannot exceed no. of observations in the corresponding cluster in`sim_IMIFA_data`

. - Slight speed-up to updating MGP hyperparameters in the presence of empty MIFA/OMIFA/IMIFA components.
- Slight speed-up to sampling cluster labels with slice indicators for IM(I)FA models.
- Explicitly allowed Pitman-Yor special case where
`alpha=0`

for IM(I)FA models;

added related controls on spike-and-slab prior for`discount`

when fixing`alpha<=0`

. - Allowed full range of
`hc`

model types for initialisation purposes via`...`

in`mixfaControl`

. - Clarified
`dimnames`

of`get_IMIFA_results`

output in`x$Loadings`

&`x$Scores`

. - Fixed storage switches & iteration indices to better account for
`burnin=0`

. - Fixed plotting of exact zeros in posterior confusion matrix.
- Fixed plotting posterior mean loadings heatmap when one or more clusters have zero factors.
- Fixed plotting scores for (I)FA models due to bug in previous update, esp. with
`zlabels`

supplied. - Fixed
`show_IMIFA_digit`

to better account for missing pixels &/or the data having been centered/scaled. - Fixed simulation of
`psi`

when not supplied to`sim_IMIFA_data`

to IG rather than GA. - Fixed bug preventing
`Q`

to be supplied to`get_IMIFA_results`

for infinite factor methods. - Fixed y-axis labelling in uncertainty type plots when
`plot.meth="zlabels"`

. - Small fixes to function
`show_digit`

. - Better handling of tied model-selection criteria in
`get_IMIFA_results`

. `Procrustes`

now works when`X`

has fewer columns than`Xstar`

.- Minor cosmetic change for overplotting
`scores`

&`loadings`

in`trace`

&`density`

plots. - Edited
`Ledermann`

and related warnings to account for case of isotropic uniquenesses. - Tidied indentation/line-breaks for
`cat`

/`message`

/`warning`

calls for printing clarity. - Corrected
`IMIFA-package`

help file (formerly just`IMIFA`

). - Edited
`CITATION`

file and authorship.

- Simplified
`mcmc_IMIFA`

by consolidating arguments using new helper functions (with defaults):- Args. common to all factor-analytic mixture methods & MCMC settings supplied via
`mixfaControl`

. - MGP & AGS args. supplied via
`mgpControl`

for infinite factor models. - Pitman-Yor/Dirichlet Process args. supplied via
`bnpControl`

for infinite mixture models. - Storage switch args. supplied via
`storeControl`

. - New functions also inherit the old documentation for their arguments.

- Args. common to all factor-analytic mixture methods & MCMC settings supplied via

- Posterior predictive checking overhauled: now MSE, RMSE etc. between empirical & estimated covariance

matrices are computed for every retained iteration so uncertainty in these estimates can be quantified:- Can be switched on/off via the
`error.metrics`

argument to`get_IMIFA_results`

. - Can be visualised by supplying
`plot.meth="errors"`

to`plot.Results_IMIFA`

. - For methods which achieve clustering, the 'overall' covariance matrix

is now properly computed from the cluster-specific covariance matrices. - Same metrics also evaluated at posterior mean parameter estimates & for final sample where possible.

- Can be switched on/off via the
`mixfaControl`

gains the arg.`prec.mu`

to control the degree of flatness of the prior for the means.- Posterior confusion matrix now returned (
`get_IMIFA_results`

) & visualisable (`plot.Results_IMIFA`

,

when`plot.meth="zlabels"`

), via new function`post_conf_mat`

, to further assess clustering uncertainty. - Added new type of clustering uncertainty profile plot in
`plot.Results_IMIFA`

when`plot.meth="zlabels"`

. - For convenience,
`get_IMIFA_results`

now also returns the last valid samples for parameters of interest,

after conditioning on the modal G & Q values and accounting for label switching and Procrustes rotation. `plot.Results_IMIFA`

gains new arg.`show.last`

that replaces any instance of showing the posterior mean

with the last valid sample instead (i.e. when`plot.meth="means"`

or`plot.meth="parallel.coords")`

.- Added ability to constrain mixing proportions across clusters using
`equal.pro`

argument for M(I)FA models:

Modified`PGMM_dfree`

accordingly and forced non-storage of mixing proportions when`equal.pro`

is`TRUE`

. - All methods now work for univariate data also (with apt. edits to plots & uniqueness defaults etc.).

`sim_IMIFA_data`

also extended to work for univariate data, as well as sped-up.

- Retired args.
`nu`

&`nuplus1`

to`mgpControl`

, replaced by ability to specify more general gamma prior,

via new`phi.hyper`

arg. specifying shape*and*rate -`mgp_check`

has also been modified accordingly. `Zsimilarity`

sped-up via the`comp.psm`

&`cltoSim`

functions s.t. when # observations < 1000.- Matrix of posterior cluster membership probabilities now returned by
`get_IMIFA_results`

. - Modified AGS to better account for when the number of group-specific latent factors shrinks to zero.
`psi.alpha`

no longer needs to be strictly greater than 1, unless the default`psi.beta`

is invoked;

thus flatter inverse gamma priors can now be specified for the uniquenesses via`mixfaControl`

.- Added "
`hc`

" option to`z.init`

to initialise allocations via hierarchical clustering (using`mclust::hc`

). - Allowed optional args. for functions used to initialise allocations via
`...`

in`mixfaControl`

. - Added
`mu`

argument to`sim_IMIFA_data`

to allow supplying true mean parameter values directly. - Standard deviation of
`aicm`

/`bicm`

model selection criteria now computed and returned. - Speed-ups due to new
`Rfast`

utility functions:`colTabulate`

&`matrnorm`

. - Speed-ups due to utility functions from
`matrixStats`

, on which`IMIFA`

already depends. - Slight improvements when
`adapt=FALSE`

for infinite factor models with fixed high truncation level. - Misclassified observations now highlighted in 1
^{st}type of uncertainty plot in`Plot.Results_IMIFA`

,

when`plot.meth="zlabels"`

and the true`zlabels`

are supplied. `mixfaControl`

gains arg.`drop0sd`

to control removal of zero-variance features (defaults to`TRUE`

).`heat_legend`

gains`cex.lab`

argument to control magnification of legend text.`mat2cols`

gains the`transparency`

argument.- Edited
`PGMM_dfree`

to include the 4 extra models from the EPGMM family.

- Supplying
`zlabels`

to`get_IMIFA_results`

will now match the cluster labels and parameters to

the true labels even if there is a mismatch between the number of clusters in both. - Similarly, supplying
`zlabels`

to`plot.Results_IMIFA`

when`plot.meth="zlabels"`

no longer does

any matching when printing performance metrics to the screen - previously this caused confusion

as associated parameters were not also permuted as they are within`get_IMIFA_results`

: now

`plot(get_IMIFA_results(sim), plot.meth="zlabels", zlabels=z)`

gives different results from

`plot(get_IMIFA_results(sim, zlabels=z), plot.meth="zlabels")`

as only the latter will permute. - Accounted for errors in covariance matrix when deriving default
`sigma.mu`

&`psi.beta`

values. - Accounted for missing empirical covariance entries within
`get_IMIFA_results`

. - Fixed model selection in
`get_IMIFA_results`

for IMFA/OMFA models when`range.Q`

is a range. - Fixed calculation of
`aicm`

,`bicm`

and`dic`

criteria: all results remain the same. - Fixed support of Ga(a, b) prior on
`alpha`

when`discount`

is being learned. - Fixed bug preventing
`uni.prior="isotropic"`

when`uni.type`

is`(un)constrained`

. - Fixed treatment of exact zeros when plotting average clustering similarity matrix.
- Fixed tiny bug when neither centering nor scaling (of any kind) are applied to data within
`mcmc_IMIFA`

. - Fixed plotting of posterior mean scores when one or more clusters are empty.
- Fixed bug with default plotting palette for data sets with >1024 variables.
- Fixed bug with label switching permutations in
`get_IMIFA_results`

when there are empty clusters. - Fixed
`print`

and`summary`

functions for objects of class`IMIFA`

and`Results_IMIFA`

. - Fixed calculating posterior mean
`zeta`

when adaptively targeting`alpha`

's optimal MH acceptance rate. - Allowed
`alpha`

be tiny for (O)M(I)FA models (provided`z.init != "priors"`

for overfitted models). - Normalised mixing proportions in
`get_IMIFA_results`

when conditioning on`G`

for IM(I)FA/OM(I)FA models. - New controls/warnings for excessively small Gamma hyperparemeters for uniqueness/local shrinkage priors.
- Clarified recommendation in
`MGP_check`

that`alpha.d2`

be moderately large relative to`alpha.d1`

. - Ensured
`sigma.mu`

hyperparameter arg. is always coerced to diagonal entries of a covariance matrix. - Transparency default in
`plot.Results_IMIFA`

now depends on device's support of semi-transparency. - Replaced certain instances of
`is.list(x)`

with`inherits(x, "list")`

for stricter checking. - Added
`check.margin=FALSE`

to calls to`sweep`

. `Ledermann`

,`MGP_check`

, and`PGMM_dfree`

are now properly vectorised.

- Added
`USPSdigits`

data set (training and test),

with associated utility functions`show_digit`

and`show_IMIFA_digit`

. - Optimised compression of
`olive`

,`coffee`

and vignette data and used`LazyData: true`

. - Added
`call.=FALSE`

to`stop()`

messages and`immediate.=TRUE`

to certain`warning()`

calls. - Removed dependency on
`adrop`

,`e1071`

,`graphics`

,`grDevices`

,`plotrix`

,`stats`

&`utils`

libraries. - Reduced dependency on
`Rfast`

w/ own versions of`colVars`

,`rowVars`

, &`standardise`

. - Added utility function
`IMIFA_news`

for accessing this`NEWS`

file. - Added
`CITATION`

file. - Extensively improved package documentation:
- Added
`Collate:`

field to`DESCRIPTION`

file. - Added line-breaks to
`usage`

sections of multi-argument functions. - Consolidated help files for
`G_expected`

&`G_variance`

.

- Added

- Fixed bug preventing M(I)FA models from being treated as (I)FA models when
`range.G`

contains 1. - Fixed bug preventing
`get_IMIFA_results`

from working properly when true labels are NOT supplied.

- Added options
`"constrained"`

&`"single"`

to`mcmc_IMIFA`

's`uni.type`

argument:

as well as being either diagonal or isotropic (UUU / UUC), uniquenesses can now further be

constrained across clusters (UCU / UCC), with appropriate warnings, defaults, checks,

initialisations, computation of model choice penalties, and plotting behaviour in all 4 cases. `mcmc_IMIFA`

gains the`tune.zeta`

argument, a list of`heat`

,`lambda`

&`target`

parameters, to invoke

diminishing adaptation for tuning the uniform proposal to achieve a target acceptance rate when`alpha`

is learned via Metropolis-Hastings when the Pitman-Yor Process prior is employed for the IM(I)FA models.

- (I)FA models sped up by considering uniquenesses under 1-cluster models as
`"constrained"`

or`"single"`

,

rather than previously`"unconstrained"`

or`"isotropic"`

, utilising pre-computation and empty assignment. - Previously hidden functions improved, exported and documented with examples:

`is.cols`

,`Ledermann`

,`Procrustes`

&`shift_GA`

. `is.posi_def`

gains`make`

argument, merging it with previously hidden function`.make_posdef`

:

Thus the 'nearest' positive-(semi)definite matrix and the usual check can be returned in a single call.- Sped-up sampling IM(I)FA labels, esp. when 'active' G falls to 1, or the
*dependent*slice-sampler is used:

`log.like`

arg. removed from`gumbel_max`

; function stands alone, now only stored log-likelihoods computed. `psi`

argument added to`sim_IMIFA_data`

to allow supplying true uniqueness parameter values directly.

- Used
`bw="SJ"`

everywhere`density`

is invoked for plotting (`bw="nrd0"`

is invoked if this fails). - Fixed initialisation of uniquenesses for
`isotropic`

(I)FA models. - Fixed parallel coordinates plot axes and labels for all
`isotropic`

uniquenesses plots. - Fixed adaptation for MIFA/OMIFA/IMIFA models when all clusters simultaneously have zero factors.
- Fixed storage bug in IM(I)FA models when
`learn.d`

is`TRUE`

but`learn.alpha`

is`FALSE`

. - Fixed density plot for
`discount`

when mutation rate is too low (i.e. too many zeros). - Fixed simulation of loadings matrices for empty MIFA/OMIFA/IMIFA clusters using
`byrow=TRUE`

:

Loop to simulate loadings matrices now generally faster also for all models. - Fixed silly error re: way in which (I)FA models are treated as 1-cluster models to ensure they run:

Related bug fixed for OM(I)FA/IM(I)FA models when starting number of clusters is actually supplied.

- Posterior mean scores can now also be plotted in the form of a heat map (previously loadings only).

`load.meth`

argument replaced by logical`heat.map`

in`plot.Results_IMIFA`

. `mat2cols`

gains`compare`

argument to yield common palettes/breaks for heat maps of multiple matrices:

Associated`plot_cols`

function also fixed, and now unhidden.- Removed certain dependencies with faster personal code: e.g. Procrustes rotation now quicker:

`IMIFA`

no longer depends on the`corpcor`

,`gclus`

,`MASS`

,`matrixcalc`

, or`MCMCpack`

libraries.

- Used
`par()$bg`

(i.e. default`"white"`

) for plotting zero-valued entries of similarity matrix. - Range of data for labelling in
`heat_legend`

calculated correctly. `mcmc_IMIFA`

's`verbose`

argument now governs printing of`message`

&`cat`

calls, but not`stop`

or`warning`

.- Fixed storage and plotting of loadings, particularly when some but not all clusters have zero factors.
- Added
`NEWS.md`

to build.

- Learning the Pitman-Yor
`discount`

&`alpha`

parameters via Metropolis-Hastings now implemented.- Spike-and-slab prior specified for
`discount`

: size of spike controlled by arg.`kappa`

. - Plotting function's
`param`

argument gains the option`discount`

for posterior inference.

- Spike-and-slab prior specified for
- Sped up simulating cluster labels from unnormalised log probabilities using the Gumbel-Max trick (Yellott, 1977):

`gumbel_max`

replaces earlier function to sample cluster labels and is now unhidden/exported/documented. - Added new plot when
`plot.meth=GQ`

for OM(I)FA/IM(I)FA models depicting trace of #s of active/non-empty clusters. - Added function
`Zsimilarity`

to summarise posterior clustering by the sampled labels with minimum

squared distance to a sparse similarity matrix constructed by averaging the adjacency matrices:

when optionally called inside`get_IMIFA_results`

, the similarity matrix can be plotted via`plot.meth="zlabels"`

.

- Metropolis-Hastings updates implemented for
`alpha`

when`discount`

is non-zero, rather than usual Gibbs.

Mutation rate monitored rather than acceptance rate for Metropolis-Hastings updates of`discount`

parameter. - Fixed calculation of # '
*free*' parameters for`aic.mcmc`

&`bic.mcmc`

criteria when uniquenesses are isotropic:

`PGMM_dfree`

, which calculates # 'free' parameters for*finite*factor analytic mixture models is exported/documented.

This function is also used to add checks on the Dirichlet hyperparameter for OM(I)FA methods. - DIC model selection criterion now also available for infinite factor models (previously finite only).
`G_priorDensity`

now better reflects discrete nature of the density, and plots for non-zero PY discount values.- Posterior mean loadings heatmaps now also display a colour key legend via new function
`heat_legend`

. - Avoided redundant simulation of stick-breaking/mixing proportions under both types of IM(I)FA slice sampler.
- Simulated (finite) mixing proportions w/
*Gamma(alpha, 1)*trick (Devroye 1986, p.594) instead of`MCMCpack:rdirichlet`

:

`rDirichlet`

replaces earlier function to sample mixing proportions and is now unhidden/exported/documented. - Deferred setting
`dimnames`

attributes in`mcmc_IMIFA`

to`get_IMIFA_results`

: lower memory burden/faster simulations. - Jettisoned superfluous duplicate material in object outputted from
`get_IMIFA_results`

to reduce size/simplify access. - Restored the IMFA/IMIFA arg.
`trunc.G`

, the max allowable # active clusters, and # active clusters now stored. - Code sped up when
`active`

G=1 by not simulating labels for IM(I)FA models. - Reduced chance of crash by exceeding memory capacity;
`score.switch`

defaults to`FALSE`

if # models ran is large.

- 2
^{nd}IM(I)FA label switching move sped up/properly weighted to ensure uniform sampling of neighbouring cluster pairs. - Offline label switching square assignment correction now permutes properly.
- Fixed factor score trace plots by extracting indices of stored samples using
`Rfast::sort_unique`

and rotating properly. - Fixed adding of
`rnorm`

columns to scores matrix during adaptation, esp. when widest loadings matrix grows/shrinks. - Fixed initialisation (and upper limit) of number of clusters for OM(I)FA/IM(I)FA, esp. when
`N < P`

. - Updates of DP/PY
`alpha`

parameter now correctly depend on current # non-empty rather than active clusters. - Fixed density plots for parameters with bounded support, accounting for spike at zero for
`discount`

. - Slightly rearranged order Gibbs updates take place, esp. to ensure means enter simulation of uniquenesses properly.
- Edited/robustified subsetting of large objects when storing
`mcmc_IMIFA`

output. - Tightened controls for when certain parameters are not stored for posterior inference.
- Edited Ledermann upper bound
`stop(...)`

for finite factor models to`warning(...)`

. - Geometric rather than arithmetic mean used to derive single rate hyperparameter for PPCA's isotropic uniquenesses.
- Uniquenesses now stored correctly for all clustering methods.
- Indices of uncertain obs. returned (
`get_IMIFA_results`

)/printed (`plot.Results_IMIFA`

) even when`zlabels`

not supplied. - Fixed behaviour of progress bar when
`verbose=FALSE`

. - Fixed typos and expanded/clarified help documentation/vignette.