Package: bamlss 1.2-5
Nikolaus Umlauf
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:
bamlss_1.2-5.tar.gz
bamlss_1.2-5.tar.gz(r-4.5-noble)bamlss_1.2-5.tar.gz(r-4.4-noble)
bamlss_1.2-5.tgz(r-4.4-emscripten)bamlss_1.2-5.tgz(r-4.3-emscripten)
bamlss.pdf |bamlss.html✨
bamlss/json (API)
NEWS
| # Install 'bamlss' in R: |
| install.packages('bamlss', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Pkgdown site:https://www.bamlss.org
- Golf - Prices of Used Cars Data
- LondonBoroughs - London Fire Data
- LondonBoundaries - London Fire Data
- LondonFStations - London Fire Data
- LondonFire - London Fire Data
- TempIbk - Temperature data.
- fatalities - Weekly Number of Fatalities in Austria
- simdata - Reference data.
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 months agofrom:0a2a4154e0. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 11 2024 |
| R-4.5-linux-x86_64 | OK | Dec 11 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
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| 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' Object | BAMLSS 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 Functions | bamlss.engine.helpers get.par get.state set.par set.starting.values |
| BAMLSS Engine Setup Function | bamlss.engine.setup |
| Formulae for BAMLSS | bamlss.formula |
| Create a Model Frame for BAMLSS | bamlss.frame |
| Bootstrap Boosting | bboost bboost_plot predict.bboost |
| Some Shortcuts | bayesx2 boost2 lasso2 |
| Compute 95% Credible Interval and Mean | c95 |
| Extract BAMLSS Coefficients | coef.bamlss confint.bamlss |
| Plot a Color Legend | colorlegend |
| Continue Sampling | continue |
| Cox Model Prediction | cox_predict |
| Crazy simulated data | Crazy |
| Continuous Rank Probability Score | CRPS |
| Deep Distributional Neural Network | cv_ddnn ddnn predict.ddnn |
| Deviance Information Criterion | DIC |
| Cholesky MVN (disttree) | dist_mvnchol |
| Show Available Engines for a Family Object | engines |
| 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 Austria | fatalities |
| BAMLSS Fitted Values | fitted.bamlss |
| GAM Artificial Data Set | GAMart |
| Extract Distribution families of the 'gamlss.dist' Package | gamlss_distributions |
| Get a BAMLSS Family | gF |
| Prices of Used Cars Data | Golf |
| HOMSTART Precipitation Data | homstart_data |
| Fit Flexible Additive Joint Models | jm_bamlss jm_mcmc jm_mode jm_predict jm_survplot jm_transform opt_JM sam_JM |
| Lasso Smooth Constructor | la lasso lasso_coef lasso_plot lasso_stop lasso_transform opt_lasso |
| Linear Effects for BAMLSS | lin smooth.construct.linear.smooth.spec |
| London Fire Data | LondonBoroughs LondonBoundaries LondonFire LondonFStations |
| Formula Generator | make_formula |
| BAMLSS Model Frame | bamlss.model.frame model.frame.bamlss model.frame.bamlss.frame |
| Construct/Extract BAMLSS Design Matrices | model.matrix.bamlss.formula model.matrix.bamlss.frame model.matrix.bamlss.terms |
| Cholesky MVN | mvn_chol |
| Modified Cholesky MVN | mvn_modchol |
| Cholesky MVN | mvnchol_bamlss |
| Neural Networks for BAMLSS | make_weights n n.weights predictn |
| Compute a Neighborhood Matrix from Spatial Polygons | neighbormatrix plotneighbors |
| Batchwise Backfitting | bbfit bbfitp contribplot opt_bbfit opt_bbfitp |
| Fit BAMLSS with Backfitting | bfit bfit_glmnet bfit_iwls bfit_iwls_lm bfit_iwls_Matrix bfit_iwls_optim bfit_lm bfit_optim opt_bfit |
| Boosting BAMLSS | boost boostm boost_frame boost_plot boost_summary opt_boost opt_boostm plot.boost_summary print.boost_summary |
| Cox Model Posterior Mode Estimation | cox_mode opt_Cox |
| Implicit Stochastic Gradient Descent Optimizer | isgd opt_isgd |
| Extract or Initialize Parameters for BAMLSS | parameters |
| Plot Coefficients Paths | pathplot |
| Plotting BAMLSS | plot.bamlss plot.bamlss.results |
| Plot 2D Effects | plot2d plotnonp |
| Plot 3D Effects | plot3d |
| Factor Variable and Random Effects Plots | plotblock |
| Plot Maps | plotmap |
| BAMLSS Prediction | predict.bamlss |
| Transform Smooth Constructs to Random Effects | randomize trans_random |
| Random Bits for BAMLSS | rb smooth.construct.randombits.smooth.spec |
| Compute BAMLSS Residuals | plot.bamlss.residuals residuals.bamlss |
| Extract the reponse name of a 'bamlss.frame' object. | response_name |
| Compute BAMLSS Results for Plotting and Summaries | results.bamlss.default |
| Remove Special Characters | rmf |
| Special Smooths in BAMLSS Formulae | s2 |
| 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 Carlo | cox_mcmc sam_Cox |
| General Markov Chain Monte Carlo for BAMLSS | GMCMC GMCMC_iwls GMCMC_iwlsC GMCMC_iwlsC_gp GMCMC_slice sam_GMCMC |
| Markov Chain Monte Carlo for BAMLSS using JAGS | BUGSeta BUGSmodel JAGS sam_JAGS |
| Create Samples for BAMLSS by Multivariate Normal Approximation | MVNORM sam_MVNORM |
| Extract Samples | samples samples.bamlss samples.bamlss.frame |
| Sampling Statistics | samplestats |
| Scaling Vectors and Matrices | scale2 |
| Reference data. | simdata |
| Simulate longitudinal and survival data for joint models | rJM simJM |
| Simulate Survival Times | rSurvTime2 simSurv |
| Plot Slices of Bivariate Functions | sliceplot |
| MCMC Based Simple Significance Check for Smooth Terms | smooth_check |
| Constructor Functions for Smooth Terms in BAMLSS | smooth.construct smooth.construct.bamlss.formula smooth.construct.bamlss.frame smooth.construct.bamlss.terms |
| Kriging Smooth Constructor | Predict.matrix.kriging.smooth smooth.construct.kr.smooth.spec |
| Smooth constructor for monotonic P-splines | smooth.construct.ms.smooth.spec |
| Random Effects P-Spline | smooth.construct.sr.smooth.spec |
| Stability selection. | plot.stabsel stabsel |
| Summary for BAMLSS | print.summary.bamlss summary.bamlss |
| Survival Model Transformer Function | surv_transform |
| Create a Survival Object for Joint Models | Surv2 |
| Temperature data. | TempIbk |
| BAMLSS Model Terms | terms.bamlss terms.bamlss.formula terms.bamlss.frame |
| AR1 Transformer Function | AR1 trans_AR1 |
| Artificial Data Set based on Auckland's Maunga Whau Volcano | Volcano |
| Watanabe-Akaike Information Criterion (WAIC) | WAIC |
