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()

bfit() bfit_iwls() bfit_iwls_Matrix() bfit_lm() bfit_optim() bfit_glmnet()

Fit BAMLSS with Backfitting

boost() boostm() boost_summary() boost_plot() print(<boost_summary>) plot(<boost_summary>) boost_frame()

Boosting BAMLSS

cox_mode()

Cox Model Posterior Mode Estimation

jm_bamlss() jm_transform() jm_mode() jm_mcmc() jm_predict() jm_survplot()

Fit Flexible Additive Joint Models

stabsel() plot(<stabsel>)

Stability selection.

bayesx2() boost2() lasso2()

Some Shortcuts

bboost() bboost_plot() predict(<bboost>)

Bootstrap Boosting

bbfit() bbfitp()

Batchwise Backfitting

Samplers

Sampler functions that can be used for model fitting using bamlss()

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

JAGS() BUGSeta() BUGSmodel()

Markov Chain Monte Carlo for BAMLSS using JAGS

MVNORM()

Create Samples for BAMLSS by Multivariate Normal Approximation

cox_mcmc()

Cox Model Markov Chain Monte Carlo

GMCMC() GMCMC_iwls() GMCMC_iwlsC() GMCMC_iwlsC_gp() GMCMC_slice()

General Markov Chain Monte Carlo for BAMLSS

jm_bamlss() jm_transform() jm_mode() jm_mcmc() jm_predict() jm_survplot()

Fit Flexible Additive Joint Models

Special Model Terms

la() 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

GAMart()

GAM Artificial Data Set

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

Misc

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

cox_predict()

Cox Model Prediction

dl.bamlss() predict(<dl.bamlss>)

Deep Learning BAMLSS

ALD_bamlss() beta_bamlss() binomial_bamlss() cnorm_bamlss() cox_bamlss() dw_bamlss() gaussian_bamlss() gaussian2_bamlss() Gaussian_bamlss() gamma_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() family(<bamlss>) family(<bamlss.frame>)

Distribution Families in bamlss

fitted(<bamlss>)

BAMLSS Fitted Values

gF()

Get a BAMLSS Family

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

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

WAIC()

Watanabe-Akaike Information Criterion (WAIC)