Bayesian Additive Models for Location, Scale, and Shape (and Beyond)
Plot Coefficients Paths
AR1 Transformer Function
Bayesian Additive Models for Location Scale and Shape (and Beyond)
BAMLSS Engine Helper Functions
BAMLSS Engine Setup Function
Formulae for BAMLSS
Create a Model Frame for BAMLSS
Fit Bayesian Additive Models for Location Scale and Shape (and Beyond)
Markov Chain Monte Carlo for BAMLSS using BayesX
Batchwise Backfitting
Bootstrap Boosting
Fit BAMLSS with Backfitting
Boosting BAMLSS
Compute 95% Credible Interval and Mean
Extract BAMLSS Coefficients
Plot a Color Legend
Continue Sampling
Cox Model Markov Chain Monte Carlo
Cox Model Posterior Mode Estimation
Cox Model Prediction
Crazy simulated data
Continuous Rank Probability Score
Deep Distributional Neural Network
Deviance Information Criterion
Cholesky MVN (disttree)
Show Available Engines for a Family Object
Distribution Families in bamlss
BAMLSS Fitted Values
GAM Artificial Data Set
Extract Distribution families of the gamlss.dist Package
Get a BAMLSS Family
General Markov Chain Monte Carlo for BAMLSS
HOMSTART Precipitation Data
Implicit Stochastic Gradient Descent Optimizer
Markov Chain Monte Carlo for BAMLSS using JAGS
Fit Flexible Additive Joint Models
Kriging Smooth Constructor
Lasso Smooth Constructor
Linear Effects for BAMLSS
Formula Generator
BAMLSS Model Frame
Construct/Extract BAMLSS Design Matrices
Cholesky MVN
Modified Cholesky MVN
Cholesky MVN
Create Samples for BAMLSS by Multivariate Normal Approximation
Neural Networks for BAMLSS
Compute a Neighborhood Matrix from Spatial Polygons
Extract or Initialize Parameters for BAMLSS
Plotting BAMLSS
Plot 2D Effects
Plot 3D Effects
Factor Variable and Random Effects Plots
Plot Maps
BAMLSS Prediction
Transform Smooth Constructs to Random Effects
Random Bits for BAMLSS
Compute BAMLSS Residuals
Extract the reponse name of a bamlss.frame object.
Compute BAMLSS Results for Plotting and Summaries
Remove Special Characters
Special Smooths in BAMLSS Formulae
Extract Samples
Sampling Statistics
Scaling Vectors and Matrices
Some Shortcuts
Simulate longitudinal and survival data for joint models
Simulate Survival Times
Plot Slices of Bivariate Functions
Smooth constructor for monotonic P-splines
Constructor Functions for Smooth Terms in BAMLSS
MCMC Based Simple Significance Check for Smooth Terms
Random Effects P-Spline
Stability selection.
Summary for BAMLSS
Survival Model Transformer Function
Create a Survival Object for Joint Models
BAMLSS Model Terms
Create distributions3 Object
Artificial Data Set based on Auckland's Maunga Whau Volcano
Watanabe-Akaike Information Criterion (WAIC)
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>.