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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 (2019) <arXiv:1909.11784>.

Functions (79)

Austria

Austria States and Topography

BayesX

Markov Chain Monte Carlo for BAMLSS using BayesX

CRPS

Continuous Rank Probability Score

Crazy

Crazy simulated data

DIC

Deviance Information Criterion

GAMart

GAM Artificial Data Set

GEFCom2014

GEFCom2014 Electricity Load Data

GMCMC

General Markov Chain Monte Carlo for BAMLSS

Golf

Prices of Used Cars Data

JAGS

Markov Chain Monte Carlo for BAMLSS using JAGS

LondonFire

London Fire Data

MVNORM

Create Samples for BAMLSS by Multivariate Normal Approximation

Surv2

Create a Survival Object for Joint Models

Volcano

Artificial Data Set based on Auckland's Maunga Whau Volcano

WAIC

Watanabe-Akaike Information Criterion (WAIC)

bamlss

Fit Bayesian Additive Models for Location Scale and Shape (and Beyond)

bamlss-package

Bayesian Additive Models for Location Scale and Shape (and Beyond)

bamlss.engine.helpers

BAMLSS Engine Helper Functions

bamlss.engine.setup

BAMLSS Engine Setup Function

bamlss.formula

Formulae for BAMLSS

bamlss.frame

Create a Model Frame for BAMLSS

bbfit

Batchwise Backfitting

bboost

Bootstrap Boosting

bfit

Fit BAMLSS with Backfitting

boost

Boosting BAMLSS

c95

Compute 95% Credible Interval and Mean

coef.bamlss

Extract BAMLSS Coefficients

colorlegend

Plot a Color Legend

continue

Continue Sampling

cox.mcmc

Cox Model Markov Chain Monte Carlo

cox.mode

Cox Model Posterior Mode Estimation

cox.predict

Cox Model Prediction

dl.bamlss

Deep Learning BAMLSS

engines

Show Available Engines for a Family Object

family.bamlss

Distribution Families in bamlss

fatalities

Weekly Number of Fatalities in Austria

fitted.bamlss

BAMLSS Fitted Values

Get a BAMLSS Family

gamlss_distributions

Extract Distribution families of the gamlss.dist Package

homstart_data

HOMSTART Precipitation Data

isgd

Implicit Stochastic Gradient Descent Optimizer

jm_bamlss

Fit Flexible Additive Joint Models

Kriging Smooth Constructor

lasso

Lasso Smooth Constructor

lin

Linear Effects for BAMLSS

model.frame.bamlss

BAMLSS Model Frame

model.matrix.bamlss.frame

Construct/Extract BAMLSS Design Matrices

Neural Networks for BAMLSS

neighbormatrix

Compute a Neighborhood Matrix from Spatial Polygons

parameters

Extract or Initialize Parameters for BAMLSS

pathplot

Plot Coefficients Paths

plot.bamlss

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

Random Bits for BAMLSS

residuals.bamlss

Compute BAMLSS Residuals

response_name

Extract the reponse name of a bamlss.frame object.

results.bamlss.default

Compute BAMLSS Results for Plotting and Summaries

rmf

Remove Special Characters

Special Smooths in BAMLSS Formulae

samples

Extract Samples

samplestats

Sampling Statistics

scale2

Scaling Vectors and Matrices

shortcuts

Some Shortcuts

simJM

Simulate longitudinal and survival data for joint models

simSurv

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_check

MCMC Based Simple Significance Check for Smooth Terms

Random Effects P-Spline

stabsel

Stability selection.

summary.bamlss

Summary for BAMLSS

surv.transform

Survival Model Transformer Function

terms.bamlss

BAMLSS Model Terms

bamlss

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

v1.1-3

GPL-2 | GPL-3

Authors

Nikolaus Umlauf [aut, cre] (<https://orcid.org/0000-0003-2160-9803>), Nadja Klein [aut] (<https://orcid.org/0000-0002-5072-5347>), Achim Zeileis [aut] (<https://orcid.org/0000-0003-0918-3766>), Meike Koehler [ctb], Thorsten Simon [aut] (<https://orcid.org/0000-0002-3778-7738>), Stanislaus Stadlmann [ctb]

Initial release

2021-01-25

bamlss

Functions (79)

bamlss

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