R: Complete Environment for Bayesian Inference

LaplacesDemon-package {LaplacesDemon} | R Documentation

## Complete Environment for Bayesian Inference

### Description

Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview). The README describes the history of the package development process.

### Details

The DESCRIPTION file:

Package: | LaplacesDemon
Version: | 16.1.1
Title: | Complete Environment for Bayesian Inference
Authors@R: | c(person("Byron", "Hall", role = "aut"), person("Martina", "Hall", role = "aut"), person(family="Statisticat, LLC", role = "aut"), person(given="Eric", family="Brown", role = "ctb"), person(given="Richard", family="Hermanson", role = "ctb"), person(given="Emmanuel", family="Charpentier", role = "ctb"), person(given="Daniel", family="Heck", role = "ctb"), person(given="Stephane", family="Laurent", role = "ctb"), person(given="Quentin F.", family="Gronau", role = "ctb"), person(given="Henrik", family="Singmann", email="singmann+LaplacesDemon@gmail.com", role="cre"))
Depends: | R (>= 3.0.0)
Imports: | parallel, grDevices, graphics, stats, utils
Suggests: | KernSmooth
ByteCompile: | TRUE
Description: | Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview). The README describes the history of the package development process.
License: | MIT + file LICENSE
URL: | https://github.com/LaplacesDemonR/LaplacesDemon
BugReports: | https://github.com/LaplacesDemonR/LaplacesDemon/issues
Author: | Byron Hall [aut], Martina Hall [aut], Statisticat, LLC [aut], Eric Brown [ctb], Richard Hermanson [ctb], Emmanuel Charpentier [ctb], Daniel Heck [ctb], Stephane Laurent [ctb], Quentin F. Gronau [ctb], Henrik Singmann [cre]
Maintainer: | Henrik Singmann <singmann+LaplacesDemon@gmail.com>

Index of help topics:

    ABB                     Approximate Bayesian Bootstrap
    AcceptanceRate          Acceptance Rate
    BMK.Diagnostic          BMK Convergence Diagnostic
    BayesFactor             Bayes Factor
    BayesTheorem            Bayes' Theorem
    BayesianBootstrap       The Bayesian Bootstrap
    BigData                 Big Data
    Blocks                  Blocks
    CSF                     Cumulative Sample Function
    CenterScale             Centering and Scaling
    Combine                 Combine Demonoid Objects
    Consort                 Consort with Laplace's Demon
    Cov2Prec                Precision
    ESS                     Effective Sample Size due to Autocorrelation
    GIV                     Generate Initial Values
    GaussHermiteQuadRule    Math Utility Functions
    Gelfand.Diagnostic      Gelfand's Convergence Diagnostic
    Gelman.Diagnostic       Gelman and Rubin's MCMC Convergence Diagnostic
    Geweke.Diagnostic       Geweke's Convergence Diagnostic
    Hangartner.Diagnostic   Hangartner's Convergence Diagnostic
    Heidelberger.Diagnostic
                            Heidelberger and Welch's MCMC Convergence
                            Diagnostic
    IAT                     Integrated Autocorrelation Time
    Importance              Variable Importance
    IterativeQuadrature     Iterative Quadrature
    Juxtapose               Juxtapose MCMC Algorithm Inefficiency
    KLD                     Kullback-Leibler Divergence (KLD)
    KS.Diagnostic           Kolmogorov-Smirnov Convergence Diagnostic
    LML                     Logarithm of the Marginal Likelihood
    LPL.interval            Lowest Posterior Loss Interval
    LaplaceApproximation    Laplace Approximation
    LaplacesDemon           Laplace's Demon
    LaplacesDemon-package   Complete Environment for Bayesian Inference
    LaplacesDemon.RAM       LaplacesDemon RAM Estimate
    Levene.Test             Levene's Test
    LossMatrix              Loss Matrix
    MCSE                    Monte Carlo Standard Error
    MISS                    Multiple Imputation Sequential Sampling
    MinnesotaPrior          Minnesota Prior
    Mode                    The Mode(s) of a Vector
    Model.Spec.Time         Model Specification Time
    PMC                     Population Monte Carlo
    PMC.RAM                 PMC RAM Estimate
    PosteriorChecks         Posterior Checks
    Raftery.Diagnostic      Raftery and Lewis's diagnostic
    RejectionSampling       Rejection Sampling
    SIR                     Sampling Importance Resampling
    SensitivityAnalysis     Sensitivity Analysis
    Stick                   Truncated Stick-Breaking
    Thin                    Thin
    Validate                Holdout Validation
    VariationalBayes        Variational Bayes
    WAIC                    Widely Applicable Information Criterion
    as.covar                Proposal Covariance
    as.indicator.matrix     Matrix Utility Functions
    as.initial.values       Initial Values
    as.parm.names           Parameter Names
    as.ppc                  As Posterior Predictive Check
    burnin                  Burn-in
    caterpillar.plot        Caterpillar Plot
    cloglog                 The log-log and complementary log-log functions
    cond.plot               Conditional Plots
    dStick                  Truncated Stick-Breaking Prior Distribution
    dalaplace               Asymmetric Laplace Distribution: Univariate
    dallaplace              Asymmetric Log-Laplace Distribution
    daml                    Asymmetric Multivariate Laplace Distribution
    dbern                   Bernoulli Distribution
    dcat                    Categorical Distribution
    dcrmrf                  Continuous Relaxation of a Markov Random Field
                            Distribution
    ddirichlet              Dirichlet Distribution
    de.Finetti.Game         de Finetti's Game
    deburn                  De-Burn
    delicit                 Prior Elicitation
    demonchoice             Demon Choice Data Set
    demonfx                 Demon FX Data Set
    demonsessions           Demon Sessions Data Set
    demonsnacks             Demon Snacks Data Set
    demontexas              Demon Space-Time Data Set
    dgpd                    Generalized Pareto Distribution
    dgpois                  Generalized Poisson Distribution
    dhalfcauchy             Half-Cauchy Distribution
    dhalfnorm               Half-Normal Distribution
    dhalft                  Half-t Distribution
    dhs                     Horseshoe Distribution
    dhuangwand              Huang-Wand Distribution
    dhyperg                 Hyperprior-g Prior and Zellner's g-Prior
    dinvbeta                Inverse Beta Distribution
    dinvchisq               (Scaled) Inverse Chi-Squared Distribution
    dinvgamma               Inverse Gamma Distribution
    dinvgaussian            Inverse Gaussian Distribution
    dinvmatrixgamma         Inverse Matrix Gamma Distribution
    dinvwishart             Inverse Wishart Distribution
    dinvwishartc            Inverse Wishart Distribution: Cholesky
                            Parameterization
    dlaplace                Laplace Distribution: Univariate Symmetric
    dlaplacem               Mixture of Laplace Distributions
    dlaplacep               Laplace Distribution: Precision
                            Parameterization
    dlasso                  LASSO Distribution
    dllaplace               Log-Laplace Distribution: Univariate Symmetric
    dlnormp                 Log-Normal Distribution: Precision
                            Parameterization
    dmatrixgamma            Matrix Gamma Distribution
    dmatrixnorm             Matrix Normal Distribution
    dmvc                    Multivariate Cauchy Distribution
    dmvcc                   Multivariate Cauchy Distribution: Cholesky
                            Parameterization
    dmvcp                   Multivariate Cauchy Distribution: Precision
                            Parameterization
    dmvcpc                  Multivariate Cauchy Distribution:
                            Precision-Cholesky Parameterization
    dmvl                    Multivariate Laplace Distribution
    dmvlc                   Multivariate Laplace Distribution: Cholesky
                            Parameterization
    dmvn                    Multivariate Normal Distribution
    dmvnc                   Multivariate Normal Distribution: Cholesky
                            Parameterization
    dmvnp                   Multivariate Normal Distribution: Precision
                            Parameterization
    dmvnpc                  Multivariate Normal Distribution:
                            Precision-Cholesky Parameterization
    dmvpe                   Multivariate Power Exponential Distribution
    dmvpec                  Multivariate Power Exponential Distribution:
                            Cholesky Parameterization
    dmvpolya                Multivariate Polya Distribution
    dmvt                    Multivariate t Distribution
    dmvtc                   Multivariate t Distribution: Cholesky
                            Parameterization
    dmvtp                   Multivariate t Distribution: Precision
                            Parameterization
    dmvtpc                  Multivariate t Distribution: Precision-Cholesky
                            Parameterization
    dnorminvwishart         Normal-Inverse-Wishart Distribution
    dnormlaplace            Normal-Laplace Distribution: Univariate
                            Asymmetric
    dnormm                  Mixture of Normal Distributions
    dnormp                  Normal Distribution: Precision Parameterization
    dnormv                  Normal Distribution: Variance Parameterization
    dnormwishart            Normal-Wishart Distribution
    dpareto                 Pareto Distribution
    dpe                     Power Exponential Distribution: Univariate
                            Symmetric
    dsdlaplace              Skew Discrete Laplace Distribution: Univariate
    dsiw                    Scaled Inverse Wishart Distribution
    dslaplace               Skew-Laplace Distribution: Univariate
    dst                     Student t Distribution: Univariate
    dstp                    Student t Distribution: Precision
                            Parameterization
    dtrunc                  Truncated Distributions
    dwishart                Wishart Distribution
    dwishartc               Wishart Distribution: Cholesky Parameterization
    dyangberger             Yang-Berger Distribution
    interval                Constrain to Interval
    is.appeased             Appeased
    is.bayesfactor          Logical Check of Classes
    is.bayesian             Logical Check of a Bayesian Model
    is.constant             Logical Check of a Constant
    is.constrained          Logical Check of Constraints
    is.data                 Logical Check of Data
    is.model                Logical Check of a Model
    is.proper               Logical Check of Propriety
    is.stationary           Logical Check of Stationarity
    joint.density.plot      Joint Density Plot
    joint.pr.plot           Joint Probability Region Plot
    logit                   The logit and inverse-logit functions
    p.interval              Probability Interval
    plot.bmk                Plot Hellinger Distances
    plot.demonoid           Plot samples from the output of Laplace's Demon
    plot.demonoid.ppc       Plots of Posterior Predictive Checks
    plot.importance         Plot Variable Importance
    plot.iterquad           Plot the output of 'IterativeQuadrature'
    plot.iterquad.ppc       Plots of Posterior Predictive Checks
    plot.juxtapose          Plot MCMC Juxtaposition
    plot.laplace            Plot the output of 'LaplaceApproximation'
    plot.laplace.ppc        Plots of Posterior Predictive Checks
    plot.miss               Plot samples from the output of MISS
    plot.pmc                Plot samples from the output of PMC
    plot.pmc.ppc            Plots of Posterior Predictive Checks
    plot.vb                 Plot the output of 'VariationalBayes'
    plot.vb.ppc             Plots of Posterior Predictive Checks
    plotMatrix              Plot a Numerical Matrix
    plotSamples             Plot Samples
    predict.demonoid        Posterior Predictive Checks
    predict.iterquad        Posterior Predictive Checks
    predict.laplace         Posterior Predictive Checks
    predict.pmc             Posterior Predictive Checks
    predict.vb              Posterior Predictive Checks
    print.demonoid          Print an object of class 'demonoid' to the
                            screen.
    print.heidelberger      Print an object of class 'heidelberger' to the
                            screen.
    print.iterquad          Print an object of class 'iterquad' to the
                            screen.
    print.laplace           Print an object of class 'laplace' to the
                            screen.
    print.miss              Print an object of class 'miss' to the screen.
    print.pmc               Print an object of class 'pmc' to the screen.
    print.raftery           Print an object of class 'raftery' to the
                            screen.
    print.vb                Print an object of class 'vb' to the screen.
    server_Listening        Server Listening
    summary.demonoid.ppc    Posterior Predictive Check Summary
    summary.iterquad.ppc    Posterior Predictive Check Summary
    summary.laplace.ppc     Posterior Predictive Check Summary
    summary.miss            MISS Summary
    summary.pmc.ppc         Posterior Predictive Check Summary
    summary.vb.ppc          Posterior Predictive Check Summary

Further information is available in the following vignettes:

`BayesianInference` | Bayesian Inference (source, pdf)
`Examples` | LaplacesDemon Examples (source, pdf)
`LaplacesDemonTutorial` | LaplacesDemon Tutorial (source, pdf)

The goal of LaplacesDemon, often referred to as LD, is to provide a complete and self-contained Bayesian environment within R. For example, this package includes dozens of MCMC algorithms, Laplace Approximation, iterative quadrature, variational Bayes, parallelization, big data, PMC, over 100 examples in the “Examples” vignette, dozens of additional probability distributions, numerous MCMC diagnostics, Bayes factors, posterior predictive checks, a variety of plots, elicitation, parameter and variable importance, Bayesian forms of test statistics (such as Durbin-Watson, Jarque-Bera, etc.), validation, and numerous additional utility functions, such as functions for multimodality, matrices, or timing your model specification. Other vignettes include an introduction to Bayesian inference, as well as a tutorial.

No further development of this package is currently being done as the original maintainer has stopped working on the package. Contributions to this package are welcome at https://github.com/LaplacesDemonR/LaplacesDemon.

The main function in this package is the `LaplacesDemon` function, and the best place to start is probably with the LaplacesDemon Tutorial vignette.

### Author(s)

NA

Maintainer: NA

[Package LaplacesDemon version 16.1.1 Index]