Function reference • bamlss

Main Model Fitting Function
`bamlss()`	Fit Bayesian Additive Models for Location Scale and Shape (and Beyond)
Optimizers Optimizer functions that can be used for model fitting using `bamlss()`
`opt_bfit()` `bfit()` `bfit_iwls()` `bfit_iwls_Matrix()` `bfit_lm()` `bfit_optim()` `bfit_glmnet()`	Fit BAMLSS with Backfitting
`opt_boost()` `boost()` `opt_boostm()` `boostm()` `boost_summary()` `boost_plot()` `print(<boost_summary>)` `plot(<boost_summary>)` `boost_frame()`	Boosting BAMLSS
`opt_bbfit()` `bbfit()` `opt_bbfitp()` `contribplot()`	Batchwise Backfitting
`opt_Cox()` `cox_mode()`	Cox Model Posterior Mode Estimation
`jm_bamlss()` `jm_transform()` `opt_JM()` `jm_mode()` `sam_JM()` `jm_mcmc()` `jm_predict()` `jm_survplot()`	Fit Flexible Additive Joint Models
`stabsel()` `plot(<stabsel>)`	Stability selection.
`bboost()` `bboost_plot()` `predict(<bboost>)`	Bootstrap Boosting
`bayesx2()` `boost2()` `lasso2()`	Some Shortcuts
`opt_isgd()`	Implicit Stochastic Gradient Descent Optimizer
Samplers Sampler functions that can be used for model fitting using `bamlss()`
`sam_BayesX()` `BayesX()` `BayesX.control()` `sx()` `tx()` `tx2()` `tx3()` `tx4()` `smooth.construct(<tensorX.smooth.spec>)` `Predict.matrix(<tensorX.smooth>)` `smooth.construct(<tensorX3.smooth.spec>)` `Predict.matrix(<tensorX3.smooth>)` `quant_bamlss()` `get_BayesXsrc()`	Markov Chain Monte Carlo for BAMLSS using BayesX
`sam_JAGS()` `JAGS()` `BUGSeta()` `BUGSmodel()`	Markov Chain Monte Carlo for BAMLSS using JAGS
`sam_MVNORM()` `MVNORM()`	Create Samples for BAMLSS by Multivariate Normal Approximation
`sam_Cox()` `cox_mcmc()`	Cox Model Markov Chain Monte Carlo
`sam_GMCMC()` `GMCMC()` `GMCMC_iwls()` `GMCMC_iwlsC()` `GMCMC_iwlsC_gp()` `GMCMC_slice()`	General Markov Chain Monte Carlo for BAMLSS
`jm_bamlss()` `jm_transform()` `opt_JM()` `jm_mode()` `sam_JM()` `jm_mcmc()` `jm_predict()` `jm_survplot()`	Fit Flexible Additive Joint Models
Special Model Terms
`la()` `opt_lasso()` `lasso()` `lasso_transform()` `lasso_plot()` `lasso_stop()` `lasso_coef()`	Lasso Smooth Constructor
`lin()` `smooth.construct(<linear.smooth.spec>)`	Linear Effects for BAMLSS
`n()` `n.weights()` `make_weights()` `predictn()`	Neural Networks for BAMLSS
`rb()` `smooth.construct(<randombits.smooth.spec>)`	Random Bits for BAMLSS
`s2()`	Special Smooths in BAMLSS Formulae
Datasets
`Austria`	Austria States and Topography
`Crazy()`	Crazy simulated data
`fatalities`	Weekly Number of Fatalities in Austria
`GAMart()`	GAM Artificial Data Set
`GEFCom2014`	GEFCom2014 Electricity Load Data
`LondonFire`	London Fire Data
`Volcano()`	Artificial Data Set based on Auckland's Maunga Whau Volcano
`homstart_data()`	HOMSTART Precipitation Data
`Golf`	Prices of Used Cars Data
`simdata`	Reference data.
`TempIbk`	Temperature data.
Multivariate Normal Family
`mvnchol_bamlss()`	Cholesky MVN
`mvn_chol()`	Cholesky MVN
`mvn_modchol()`	Modified Cholesky MVN
`dist_mvnchol()`	Cholesky MVN (disttree)
`make_formula()`	Formula Generator
Misc
`BAMLSS()`	Create distributions3 Object
`DIC()`	Deviance Information Criterion
`Surv2()`	Create a Survival Object for Joint Models
`bamlss-package`	Bayesian Additive Models for Location Scale and Shape (and Beyond)
`get.par()` `get.state()` `set.par()` `set.starting.values()`	BAMLSS Engine Helper Functions
`bamlss.engine.setup()`	BAMLSS Engine Setup Function
`bamlss.formula()`	Formulae for BAMLSS
`bamlss.frame()`	Create a Model Frame for BAMLSS
`c95()`	Compute 95% Credible Interval and Mean
`coef(<bamlss>)` `confint(<bamlss>)`	Extract BAMLSS Coefficients
`colorlegend()`	Plot a Color Legend
`continue()`	Continue Sampling
`ALD_bamlss()` `beta_bamlss()` `binomial_bamlss()` `cnorm_bamlss()` `cox_bamlss()` `dw_bamlss()` `DGP_bamlss()` `dirichlet_bamlss()` `ELF_bamlss()` `gaussian_bamlss()` `gaussian2_bamlss()` `Gaussian_bamlss()` `gamma_bamlss()` `logNN_bamlss()` `multinomial_bamlss()` `mvnorm_bamlss()` `mvnormAR1_bamlss()` `poisson_bamlss()` `gpareto_bamlss()` `glogis_bamlss()` `AR1_bamlss()` `beta1_bamlss()` `nbinom_bamlss()` `ztnbinom_bamlss()` `lognormal_bamlss()` `weibull_bamlss()` `Sichel_bamlss()` `GEV_bamlss()` `gumbel_bamlss()` `mix_bamlss()` `ZANBI_bamlss()` `family(<bamlss>)` `family(<bamlss.frame>)`	Distribution Families in bamlss
`cox_predict()`	Cox Model Prediction
`CRPS()`	Continuous Rank Probability Score
`ddnn()` `predict(<ddnn>)` `cv_ddnn()`	Deep Distributional Neural Network
`engines()`	Show Available Engines for a Family Object
`gamlss_distributions()`	Extract Distribution families of the gamlss.dist Package
`fitted(<bamlss>)`	BAMLSS Fitted Values
`gF()`	Get a BAMLSS Family
`jm_bamlss()` `jm_transform()` `opt_JM()` `jm_mode()` `sam_JM()` `jm_mcmc()` `jm_predict()` `jm_survplot()`	Fit Flexible Additive Joint Models
`smooth.construct(<kr.smooth.spec>)` `Predict.matrix(<kriging.smooth>)`	Kriging Smooth Constructor
`model.frame(<bamlss>)` `model.frame(<bamlss.frame>)` `bamlss.model.frame()`	BAMLSS Model Frame
`model.matrix(<bamlss.frame>)` `model.matrix(<bamlss.formula>)` `model.matrix(<bamlss.terms>)`	Construct/Extract BAMLSS Design Matrices
`neighbormatrix()` `plotneighbors()`	Compute a Neighborhood Matrix from Spatial Polygons
`parameters()`	Extract or Initialize Parameters for BAMLSS
`pathplot()`	Plot Coefficients Paths
`plot(<bamlss>)` `plot(<bamlss.results>)`	Plotting BAMLSS
`plot2d()`	Plot 2D Effects
`plot3d()`	Plot 3D Effects
`plotblock()`	Factor Variable and Random Effects Plots
`plotmap()`	Plot Maps
`predict(<bamlss>)`	BAMLSS Prediction
`trans_random()` `randomize()`	Transform Smooth Constructs to Random Effects
`response_name()`	Extract the reponse name of a `bamlss.frame` object.
`residuals(<bamlss>)` `plot(<bamlss.residuals>)`	Compute BAMLSS Residuals
`results.bamlss.default()`	Compute BAMLSS Results for Plotting and Summaries
`rmf()`	Remove Special Characters
`samples()`	Extract Samples
`samplestats()`	Sampling Statistics
`scale2()`	Scaling Vectors and Matrices
`simJM()` `rJM()`	Simulate longitudinal and survival data for joint models
`simSurv()` `rSurvTime2()`	Simulate Survival Times
`sliceplot()`	Plot Slices of Bivariate Functions
`smooth.construct()`	Constructor Functions for Smooth Terms in BAMLSS
`smooth.construct(<ms.smooth.spec>)`	Smooth constructor for monotonic P-splines
`smooth.construct(<sr.smooth.spec>)`	Random Effects P-Spline
`smooth_check()`	MCMC Based Simple Significance Check for Smooth Terms
`summary(<bamlss>)` `print(<summary.bamlss>)`	Summary for BAMLSS
`surv_transform()`	Survival Model Transformer Function
`terms(<bamlss>)` `terms(<bamlss.frame>)` `terms(<bamlss.formula>)`	BAMLSS Model Terms
`trans_AR1()` `AR1()`	AR1 Transformer Function
`WAIC()`	Watanabe-Akaike Information Criterion (WAIC)

Reference

Main Model Fitting Function

Optimizers

Samplers

Special Model Terms

Datasets

Multivariate Normal Family

Misc