Infinite Mixtures of Infinite Factor Analysers and Related Models

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. (2018) . The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures 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.


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Infinite Mixture of Infinite Factor Analysers

(and related models)

Written by Keefe Murphy

Description

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.

Installation

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.

References

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

News

Infinite Mixtures of Infinite Factor Analysers

IMIFA v2.1.0 - (7th release [minor update]: 2019-02-04)

New Features

  • 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).

Improvements

  • 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 2nd 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().

Bug Fixes

  • 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.
  • 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.

IMIFA v2.0.0 - (6th release [major update]: 2018-05-01)

Major Changes

  • 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.

New Features

  • 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.
  • 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.

Improvements

  • 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 1st 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.

Bug Fixes

  • 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.

Miscellaneous Edits

  • 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 onadrop, 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.

IMIFA v1.3.1 - (5th release [patch update]: 2017-07-07)

Bug Fixes

  • 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.

IMIFA v1.3.0 - (4th release [minor update]: 2017-06-22)

New Features

  • 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.

Improvements

  • (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.

Bug Fixes

  • 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.

IMIFA v1.2.1 - (3rd release [patch update]: 2017-05-29)

Improvements

  • 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.

Bug Fixes

  • 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.

IMIFA v1.2.0 - (2nd release [minor update]: 2017-05-09)

New Features

  • 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.
  • 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".

Improvements

  • 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.

Bug Fixes

  • 2nd 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.

IMIFA v1.1.0 - (1st release: 2017-02-02)

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("IMIFA")

2.1.0 by Keefe Murphy, 7 months ago


https://cran.r-project.org/package=IMIFA


Report a bug at https://github.com/Keefe-Murphy/IMIFA


Browse source code at https://github.com/cran/IMIFA


Authors: Keefe Murphy [aut, cre] , Cinzia Viroli [ctb] , Isobel Claire Gormley [ctb]


Documentation:   PDF Manual  


Task views: Cluster Analysis & Finite Mixture Models


GPL (>= 2) license


Imports matrixStats, mclust, mvnfast, Rfast, slam, viridis

Suggests gmp, knitr, mcclust, methods, rmarkdown, Rmpfr


See at CRAN