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bamlss (version 1.2-5)

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

1,144

Version

1.2-5

License

GPL-2 | GPL-3

Maintainer

Nikolaus Umlauf

Last Published

October 11th, 2024

Functions in bamlss (1.2-5)

Watanabe-Akaike Information Criterion (WAIC)

Create a Survival Object for Joint Models

Create Samples for BAMLSS by Multivariate Normal Approximation

Temperature data.

bamlss.engine.helpers

BAMLSS Engine Helper Functions

Artificial Data Set based on Auckland's Maunga Whau Volcano

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

bamlss.engine.setup

BAMLSS Engine Setup Function

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

Formulae for BAMLSS

Bootstrap Boosting

Continue Sampling

Boosting BAMLSS

Batchwise Backfitting

Compute 95% Credible Interval and Mean

Plot a Color Legend

Fit BAMLSS with Backfitting

Create a Model Frame for BAMLSS

Extract BAMLSS Coefficients

Get a BAMLSS Family

Weekly Number of Fatalities in Austria

gamlss_distributions

Extract Distribution families of the gamlss.dist Package

Deep Distributional Neural Network

Cholesky MVN (disttree)

Cox Model Markov Chain Monte Carlo

Show Available Engines for a Family Object

Distribution Families in bamlss

Cox Model Posterior Mode Estimation

Cox Model Prediction

BAMLSS Fitted Values

model.matrix.bamlss.frame

Construct/Extract BAMLSS Design Matrices

Lasso Smooth Constructor

HOMSTART Precipitation Data

Implicit Stochastic Gradient Descent Optimizer

Modified Cholesky MVN

Formula Generator

Fit Flexible Additive Joint Models

Plot 3D Effects

Linear Effects for BAMLSS

Neural Networks for BAMLSS

Factor Variable and Random Effects Plots

smooth.construct.kr.smooth.spec

Kriging Smooth Constructor

Extract or Initialize Parameters for BAMLSS

model.frame.bamlss

BAMLSS Model Frame

Plot Slices of Bivariate Functions

Plotting BAMLSS

Special Smooths in BAMLSS Formulae

Extract the reponse name of a bamlss.frame object.

smooth.construct

Constructor Functions for Smooth Terms in BAMLSS

residuals.bamlss

Compute BAMLSS Residuals

Compute a Neighborhood Matrix from Spatial Polygons

Extract Samples

smooth.construct.ms.smooth.spec

Smooth constructor for monotonic P-splines

results.bamlss.default

Compute BAMLSS Results for Plotting and Summaries

Transform Smooth Constructs to Random Effects

BAMLSS Model Terms

Remove Special Characters

MCMC Based Simple Significance Check for Smooth Terms

Plot 2D Effects

Create distributions3 Object

Simulate longitudinal and survival data for joint models

Random Bits for BAMLSS

Summary for BAMLSS

Simulate Survival Times

Reference data.

Plot Coefficients Paths

Stability selection.

smooth.construct.sr.smooth.spec

Random Effects P-Spline

Survival Model Transformer Function

Sampling Statistics

BAMLSS Prediction

Scaling Vectors and Matrices

AR1 Transformer Function

Prices of Used Cars Data

General Markov Chain Monte Carlo for BAMLSS

Deviance Information Criterion

Crazy simulated data

GAM Artificial Data Set

Markov Chain Monte Carlo for BAMLSS using JAGS

London Fire Data

Markov Chain Monte Carlo for BAMLSS using BayesX

Continuous Rank Probability Score