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]

bamlss_1.2-5.tar.gz
bamlss_1.2-5.zip(r-4.7-x86_64)bamlss_1.2-5.zip(r-4.7-arm64)bamlss_1.2-5.zip(r-4.6-x86_64)bamlss_1.2-5.zip(r-4.6-arm64)bamlss_1.2-5.zip(r-4.5-x86_64)
bamlss_1.2-5.tgz(r-4.6-x86_64)bamlss_1.2-5.tgz(r-4.6-arm64)bamlss_1.2-5.tgz(r-4.5-x86_64)bamlss_1.2-5.tgz(r-4.5-arm64)
bamlss_1.2-5.tar.gz(r-4.7-arm64)bamlss_1.2-5.tar.gz(r-4.7-x86_64)bamlss_1.2-5.tar.gz(r-4.6-arm64)bamlss_1.2-5.tar.gz(r-4.6-x86_64)
bamlss_1.2-5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bamlss/json (API)

# Install 'bamlss' in R:

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

Pkgdown/docs site:https://www.bamlss.org

Uses libs:

openblas– Optimized BLAS

Datasets:

fatalities - Weekly Number of Fatalities in Austria
Golf - Prices of Used Cars Data
LondonBoroughs - London Fire Data
LondonBoundaries - London Fire Data
LondonFire - London Fire Data
LondonFStations - London Fire Data
simdata - Reference data.
TempIbk - Temperature data.

On CRAN:

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

openblas

5.76 score 2 stars 4 packages 235 scripts 2.1k downloads 5 mentions 172 exports 14 dependencies

Last updated from:0a2a4154e0. Checks:13 OK, 2 NOTE. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	261
linux-devel-x86_64	OK	249
source / vignettes	OK	286
linux-release-arm64	OK	240
linux-release-x86_64	OK	266
macos-release-arm64	OK	217
macos-release-x86_64	OK	366
macos-oldrel-arm64	NOTE	199
macos-oldrel-x86_64	NOTE	416
windows-devel-arm64	OK	320
windows-devel-x86_64	OK	196
windows-release-arm64	OK	326
windows-release-x86_64	OK	263
windows-oldrel-x86_64	OK	268
wasm-release	OK	128

Exports:ALD_bamlss AR1 AR1_bamlss bamlss BAMLSS bamlss.engine.setup bamlss.family bamlss.formula bamlss.frame bamlss.model.frame BayesX BayesX.control bayesx2 bbfit bbfitp bboost bboost_plot beta_bamlss beta1_bamlss bfit bfit_glmnet bfit_iwls bfit_iwls_Matrix bfit_lm bfit_optim binomial_bamlss boost boost_frame boost_plot boost_summary boost2 boostm BUGSeta BUGSmodel c95 cnorm_bamlss colorlegend continue contribplot cox_bamlss cox_mcmc cox_mode cox_predict Crazy CRPS cv_ddnn ddnn DGP_bamlss DIC dirichlet_bamlss dist_mvnchol dw_bamlss ELF_bamlss engines GAMart gamlss_distributions gamma_bamlss gaussian_bamlss Gaussian_bamlss gaussian2_bamlss get_BayesXsrc get.par get.state GEV_bamlss gF glogis_bamlss GMCMC GMCMC_iwls GMCMC_iwlsC GMCMC_iwlsC_gp GMCMC_slice gpareto_bamlss gumbel_bamlss homstart_data JAGS jm_bamlss jm_mcmc jm_mode jm_predict jm_survplot la lasso lasso_coef lasso_plot lasso_stop lasso_transform lasso2 lin logNN_bamlss lognormal_bamlss make_formula make_weights mix_bamlss multinomial_bamlss mvn_chol mvn_modchol mvnchol_bamlss MVNORM mvnorm_bamlss n n.weights nbinom_bamlss neighbormatrix opt_bbfit opt_bbfitp opt_bfit opt_boost opt_boostm opt_Cox opt_JM opt_lasso parameters pathplot plot2d plot3d plotblock plotmap plotneighbors poisson_bamlss predict.bboost predict.ddnn Predict.matrix.kriging.smooth Predict.matrix.tensorX.smooth Predict.matrix.tensorX3.smooth predictn quant_bamlss randomize rb response_name results.bamlss.default rJM rmf rSurvTime2 s2 sam_BayesX sam_Cox sam_GMCMC sam_JAGS sam_JM sam_MVNORM samples samplestats scale2 set.par set.starting.values Sichel_bamlss simJM simSurv sliceplot smooth_check smooth.construct smooth.construct.kr.smooth.spec smooth.construct.linear.smooth.spec smooth.construct.ms.smooth.spec smooth.construct.randombits.smooth.spec smooth.construct.tensorX.smooth.spec smooth.construct.tensorX3.smooth.spec stabsel surv_transform Surv2 sx trans_AR1 trans_random tx tx2 tx3 tx4 Volcano WAIC weibull_bamlss ZANBI_bamlss ztnbinom_bamlss

Dependencies:BH coda colorspace distributions3 Formula lattice Matrix MBA mgcv mvtnorm nlme rlang sp survival

A Lego Toolbox for Flexible Bayesian Regression (and Beyond)

Overview | Installation | Basic Bayesian regression | Location-scale model | References

Rendered frombamlss.Rmdusingknitr::rmarkdown

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

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

Package: bamlss 1.2-5

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

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