| 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).
Details
The DESCRIPTION file:
| Package: | LaplacesDemon |
| Version: | 16.1.6 |
| 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). |
| 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
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 16.1.6 Index]