Package: bamlss 1.2-5

bamlss: Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021) <doi:10.18637/jss.v100.i04>.

Authors:Nikolaus Umlauf [aut, cre], Nadja Klein [aut], Achim Zeileis [aut], Meike Koehler [ctb], Thorsten Simon [aut], Stanislaus Stadlmann [ctb], Alexander Volkmann [ctb]

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NEWS

# Install 'bamlss' in R:
install.packages('bamlss', repos = c('https://freezenik.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.91 score 1 stars 5 packages 236 scripts 1.2k downloads 5 mentions 172 exports 36 dependencies

Last updated 26 days agofrom:0a2a4154e0. Checks:OK: 3 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 12 2024
R-4.5-win-x86_64OKOct 12 2024
R-4.5-linux-x86_64OKOct 12 2024
R-4.4-win-x86_64NOTEOct 12 2024
R-4.4-mac-x86_64NOTEOct 12 2024
R-4.4-mac-aarch64NOTEOct 12 2024
R-4.3-win-x86_64NOTEOct 12 2024
R-4.3-mac-x86_64NOTEOct 12 2024
R-4.3-mac-aarch64NOTEOct 12 2024

Exports:ALD_bamlssAR1AR1_bamlssbamlssBAMLSSbamlss.engine.setupbamlss.familybamlss.formulabamlss.framebamlss.model.frameBayesXBayesX.controlbayesx2bbfitbbfitpbboostbboost_plotbeta_bamlssbeta1_bamlssbfitbfit_glmnetbfit_iwlsbfit_iwls_Matrixbfit_lmbfit_optimbinomial_bamlssboostboost_frameboost_plotboost_summaryboost2boostmBUGSetaBUGSmodelc95cnorm_bamlsscolorlegendcontinuecontribplotcox_bamlsscox_mcmccox_modecox_predictCrazyCRPScv_ddnnddnnDGP_bamlssDICdirichlet_bamlssdist_mvncholdw_bamlssELF_bamlssenginesGAMartgamlss_distributionsgamma_bamlssgaussian_bamlssGaussian_bamlssgaussian2_bamlssget_BayesXsrcget.parget.stateGEV_bamlssgFglogis_bamlssGMCMCGMCMC_iwlsGMCMC_iwlsCGMCMC_iwlsC_gpGMCMC_slicegpareto_bamlssgumbel_bamlsshomstart_dataJAGSjm_bamlssjm_mcmcjm_modejm_predictjm_survplotlalassolasso_coeflasso_plotlasso_stoplasso_transformlasso2linlogNN_bamlsslognormal_bamlssmake_formulamake_weightsmix_bamlssmultinomial_bamlssmvn_cholmvn_modcholmvnchol_bamlssMVNORMmvnorm_bamlssnn.weightsnbinom_bamlssneighbormatrixopt_bbfitopt_bbfitpopt_bfitopt_boostopt_boostmopt_Coxopt_JMopt_lassoparameterspathplotplot2dplot3dplotblockplotmapplotneighborspoisson_bamlsspredict.bboostpredict.ddnnPredict.matrix.kriging.smoothPredict.matrix.tensorX.smoothPredict.matrix.tensorX3.smoothpredictnquant_bamlssrandomizerbresponse_nameresults.bamlss.defaultrJMrmfrSurvTime2s2sam_BayesXsam_Coxsam_GMCMCsam_JAGSsam_JMsam_MVNORMsamplessamplestatsscale2set.parset.starting.valuesSichel_bamlsssimJMsimSurvsliceplotsmooth_checksmooth.constructsmooth.construct.kr.smooth.specsmooth.construct.linear.smooth.specsmooth.construct.ms.smooth.specsmooth.construct.randombits.smooth.specsmooth.construct.tensorX.smooth.specsmooth.construct.tensorX3.smooth.specstabselsurv_transformSurv2sxtrans_AR1trans_randomtxtx2tx3tx4VolcanoWAICweibull_bamlssZANBI_bamlssztnbinom_bamlss

Dependencies:BHclicodacolorspacedistributions3fansifarverFormulaggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixMBAmgcvmunsellmvtnormnlmepillarpkgconfigR6RColorBrewerrlangscalesspsurvivaltibbleutf8vctrsviridisLitewithr

A Lego Toolbox for Flexible Bayesian Regression (and Beyond)

Rendered frombamlss.Rmdusingknitr::rmarkdownon Oct 12 2024.

Last update: 2024-04-30
Started: 2019-09-27

Readme and manuals

Help Manual

Help pageTopics
Bayesian Additive Models for Location Scale and Shape (and Beyond)bamlss-package
Fit Bayesian Additive Models for Location Scale and Shape (and Beyond)bamlss
Create 'distributions3' ObjectBAMLSS cdf.BAMLSS family.BAMLSS format.BAMLSS is_continuous.BAMLSS is_discrete.BAMLSS kurtosis.BAMLSS log_pdf.BAMLSS mean.BAMLSS pdf.BAMLSS print.BAMLSS quantile.BAMLSS random.BAMLSS skewness.BAMLSS support.BAMLSS variance.BAMLSS
BAMLSS Engine Helper Functionsbamlss.engine.helpers get.par get.state set.par set.starting.values
BAMLSS Engine Setup Functionbamlss.engine.setup
Formulae for BAMLSSbamlss.formula
Create a Model Frame for BAMLSSbamlss.frame
Bootstrap Boostingbboost bboost_plot predict.bboost
Some Shortcutsbayesx2 boost2 lasso2
Compute 95% Credible Interval and Meanc95
Extract BAMLSS Coefficientscoef.bamlss confint.bamlss
Plot a Color Legendcolorlegend
Continue Samplingcontinue
Cox Model Predictioncox_predict
Crazy simulated dataCrazy
Continuous Rank Probability ScoreCRPS
Deep Distributional Neural Networkcv_ddnn ddnn predict.ddnn
Deviance Information CriterionDIC
Cholesky MVN (disttree)dist_mvnchol
Show Available Engines for a Family Objectengines
Distribution Families in 'bamlss'ALD_bamlss AR1_bamlss bamlss.family beta1_bamlss beta_bamlss binomial_bamlss cnorm_bamlss cox_bamlss DGP_bamlss dirichlet_bamlss dw_bamlss ELF_bamlss family.bamlss family.bamlss.frame gamma_bamlss gaussian2_bamlss Gaussian_bamlss gaussian_bamlss GEV_bamlss glogis_bamlss gpareto_bamlss gumbel_bamlss logNN_bamlss lognormal_bamlss mix_bamlss multinomial_bamlss mvnormAR1_bamlss mvnorm_bamlss nbinom_bamlss poisson_bamlss Sichel_bamlss weibull_bamlss ZANBI_bamlss ztnbinom_bamlss
Weekly Number of Fatalities in Austriafatalities
BAMLSS Fitted Valuesfitted.bamlss
GAM Artificial Data SetGAMart
Extract Distribution families of the 'gamlss.dist' Packagegamlss_distributions
Get a BAMLSS FamilygF
Prices of Used Cars DataGolf
HOMSTART Precipitation Datahomstart_data
Fit Flexible Additive Joint Modelsjm_bamlss jm_mcmc jm_mode jm_predict jm_survplot jm_transform opt_JM sam_JM
Lasso Smooth Constructorla lasso lasso_coef lasso_plot lasso_stop lasso_transform opt_lasso
Linear Effects for BAMLSSlin smooth.construct.linear.smooth.spec
London Fire DataLondonBoroughs LondonBoundaries LondonFire LondonFStations
Formula Generatormake_formula
BAMLSS Model Framebamlss.model.frame model.frame.bamlss model.frame.bamlss.frame
Construct/Extract BAMLSS Design Matricesmodel.matrix.bamlss.formula model.matrix.bamlss.frame model.matrix.bamlss.terms
Cholesky MVNmvn_chol
Modified Cholesky MVNmvn_modchol
Cholesky MVNmvnchol_bamlss
Neural Networks for BAMLSSmake_weights n n.weights predictn
Compute a Neighborhood Matrix from Spatial Polygonsneighbormatrix plotneighbors
Batchwise Backfittingbbfit bbfitp contribplot opt_bbfit opt_bbfitp
Fit BAMLSS with Backfittingbfit bfit_glmnet bfit_iwls bfit_iwls_lm bfit_iwls_Matrix bfit_iwls_optim bfit_lm bfit_optim opt_bfit
Boosting BAMLSSboost boostm boost_frame boost_plot boost_summary opt_boost opt_boostm plot.boost_summary print.boost_summary
Cox Model Posterior Mode Estimationcox_mode opt_Cox
Implicit Stochastic Gradient Descent Optimizerisgd opt_isgd
Extract or Initialize Parameters for BAMLSSparameters
Plot Coefficients Pathspathplot
Plotting BAMLSSplot.bamlss plot.bamlss.results
Plot 2D Effectsplot2d plotnonp
Plot 3D Effectsplot3d
Factor Variable and Random Effects Plotsplotblock
Plot Mapsplotmap
BAMLSS Predictionpredict.bamlss
Transform Smooth Constructs to Random Effectsrandomize trans_random
Random Bits for BAMLSSrb smooth.construct.randombits.smooth.spec
Compute BAMLSS Residualsplot.bamlss.residuals residuals.bamlss
Extract the reponse name of a 'bamlss.frame' object.response_name
Compute BAMLSS Results for Plotting and Summariesresults.bamlss.default
Remove Special Charactersrmf
Special Smooths in BAMLSS Formulaes2
Markov Chain Monte Carlo for BAMLSS using 'BayesX'BayesX BayesX.control get_BayesXsrc Predict.matrix.tensorX.smooth Predict.matrix.tensorX3.smooth quant_bamlss sam_BayesX smooth.construct.tensorX.smooth.spec smooth.construct.tensorX3.smooth.spec sx tx tx2 tx3 tx4
Cox Model Markov Chain Monte Carlocox_mcmc sam_Cox
General Markov Chain Monte Carlo for BAMLSSGMCMC GMCMC_iwls GMCMC_iwlsC GMCMC_iwlsC_gp GMCMC_slice sam_GMCMC
Markov Chain Monte Carlo for BAMLSS using JAGSBUGSeta BUGSmodel JAGS sam_JAGS
Create Samples for BAMLSS by Multivariate Normal ApproximationMVNORM sam_MVNORM
Extract Samplessamples samples.bamlss samples.bamlss.frame
Sampling Statisticssamplestats
Scaling Vectors and Matricesscale2
Reference data.simdata
Simulate longitudinal and survival data for joint modelsrJM simJM
Simulate Survival TimesrSurvTime2 simSurv
Plot Slices of Bivariate Functionssliceplot
MCMC Based Simple Significance Check for Smooth Termssmooth_check
Constructor Functions for Smooth Terms in BAMLSSsmooth.construct smooth.construct.bamlss.formula smooth.construct.bamlss.frame smooth.construct.bamlss.terms
Kriging Smooth ConstructorPredict.matrix.kriging.smooth smooth.construct.kr.smooth.spec
Smooth constructor for monotonic P-splinessmooth.construct.ms.smooth.spec
Random Effects P-Splinesmooth.construct.sr.smooth.spec
Stability selection.plot.stabsel stabsel
Summary for BAMLSSprint.summary.bamlss summary.bamlss
Survival Model Transformer Functionsurv_transform
Create a Survival Object for Joint ModelsSurv2
Temperature data.TempIbk
BAMLSS Model Termsterms.bamlss terms.bamlss.formula terms.bamlss.frame
AR1 Transformer FunctionAR1 trans_AR1
Artificial Data Set based on Auckland's Maunga Whau VolcanoVolcano
Watanabe-Akaike Information Criterion (WAIC)WAIC