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bamlss (version 1.1-6)

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

Description

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) and the R package in Umlauf, Klein, Simon, Zeileis (2021) .

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Version

Install

install.packages('bamlss')

Monthly Downloads

1,143

Version

1.1-6

License

GPL-2 | GPL-3

Maintainer

Nikolaus Umlauf

Last Published

November 24th, 2021

Functions in bamlss (1.1-6)

GAM Artificial Data Set

Markov Chain Monte Carlo for BAMLSS using BayesX

Prices of Used Cars Data

Deviance Information Criterion

General Markov Chain Monte Carlo for BAMLSS

Crazy simulated data

AR1 Transformer Function

Continuous Rank Probability Score

GEFCom2014 Electricity Load Data

Austria States and Topography

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

Create a Survival Object for Joint Models

Create Samples for BAMLSS by Multivariate Normal Approximation

Artificial Data Set based on Auckland's Maunga Whau Volcano

Watanabe-Akaike Information Criterion (WAIC)

bamlss.engine.helpers

BAMLSS Engine Helper Functions

bamlss.engine.setup

BAMLSS Engine Setup Function

Markov Chain Monte Carlo for BAMLSS using JAGS

London Fire Data

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

Boosting BAMLSS

Continue Sampling

Batchwise Backfitting

Plot a Color Legend

Create a Model Frame for BAMLSS

Compute 95% Credible Interval and Mean

Formulae for BAMLSS

Fit BAMLSS with Backfitting

Extract BAMLSS Coefficients

Bootstrap Boosting

Get a BAMLSS Family

Cox Model Markov Chain Monte Carlo

gamlss_distributions

Extract Distribution families of the gamlss.dist Package

Distribution Families in bamlss

Show Available Engines for a Family Object

Cox Model Prediction

Deep Learning BAMLSS

BAMLSS Fitted Values

Weekly Number of Fatalities in Austria

HOMSTART Precipitation Data

Neural Networks for BAMLSS

model.matrix.bamlss.frame

Construct/Extract BAMLSS Design Matrices

model.frame.bamlss

BAMLSS Model Frame

Lasso Smooth Constructor

Cox Model Posterior Mode Estimation

Compute a Neighborhood Matrix from Spatial Polygons

smooth.construct.kr.smooth.spec

Kriging Smooth Constructor

Implicit Stochastic Gradient Descent Optimizer

Fit Flexible Additive Joint Models

Linear Effects for BAMLSS

Extract or Initialize Parameters for BAMLSS

Plot Coefficients Paths

Transform Smooth Constructs to Random Effects

Random Bits for BAMLSS

Simulate longitudinal and survival data for joint models

BAMLSS Prediction

Remove Special Characters

Plot 2D Effects

Plotting BAMLSS

results.bamlss.default

Compute BAMLSS Results for Plotting and Summaries

Plot Slices of Bivariate Functions

Extract the reponse name of a bamlss.frame object.

Simulate Survival Times

residuals.bamlss

Compute BAMLSS Residuals

smooth.construct

Constructor Functions for Smooth Terms in BAMLSS

Stability selection.

Special Smooths in BAMLSS Formulae

Plot 3D Effects

Summary for BAMLSS

Scaling Vectors and Matrices

Factor Variable and Random Effects Plots

Sampling Statistics

smooth.construct.ms.smooth.spec

Smooth constructor for monotonic P-splines

Survival Model Transformer Function

BAMLSS Model Terms

MCMC Based Simple Significance Check for Smooth Terms

smooth.construct.sr.smooth.spec

Random Effects P-Spline

Extract Samples