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bamlss (version 1.0-2)

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 (2018) .

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Version

Install

install.packages('bamlss')

Monthly Downloads

1,195

Version

1.0-2

License

GPL-2 | GPL-3

Maintainer

Nikolaus Umlauf

Last Published

July 30th, 2019

Functions in bamlss (1.0-2)

Markov Chain Monte Carlo for BAMLSS using BayesX

Create a Survival Object for Joint Models

Create Samples for BAMLSS by Multivariate Normal Approximation

GAM Artificial Data Set

Deviance Information Criterion

London Fire Data

Artificial Data Set based on Auckland's Maunga Whau Volcano

General Markov Chain Monte Carlo for BAMLSS

Austria States and Topography

Markov Chain Monte Carlo for BAMLSS using JAGS

bamlss.engine.setup

BAMLSS Engine Setup Function

Cox Model Posterior Mode Estimation

Get a BAMLSS Family

Fit BAMLSS with Backfitting

Cox Model Prediction

BAMLSS Fitted Values

Bootstrap Boosting

Watanabe-Akaike Information Criterion (WAIC)

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

model.frame.bamlss

BAMLSS Model Frame

Extract BAMLSS Coefficients

Formulae for BAMLSS

results.bamlss.default

Compute BAMLSS Results for Plotting and Summaries

Plot 2D Effects

BAMLSS Model Terms

model.matrix.bamlss.frame

Construct/Extract BAMLSS Design Matrices

Remove Special Characters

Plot 3D Effects

Lasso Smooth Constructor

Deep Learning BAMLSS

Distribution Families in bamlss

Create a Model Frame for BAMLSS

Linear Effects for BAMLSS

bamlss.engine.helpers

BAMLSS Engine Helper Functions

Compute 95% Credible Interval and Mean

HOMSTART Precipitation Data

Boosting BAMLSS

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

Neural Networks for BAMLSS

Plot a Color Legend

Extract or Initialize Parameters for BAMLSS

Plotting BAMLSS

Compute a Neighborhood Matrix from Spatial Polygons

Batch-Wise Backfitting

Continue Sampling

smooth.construct.kr.smooth.spec

Kriging Smooth Constructor

residuals.bamlss

Compute BAMLSS Residuals

Random Bits for BAMLSS

Fit Flexible Additive Joint Models

Simulate Survival Times

Plot Slices of Bivariate Functions

Cox Model Markov Chain Monte Carlo

Special Smooths in BAMLSS Formulae

Factor Variable and Random Effects Plots

Extract Samples

Transform Smooth Constructs to Random Effects

Summary for BAMLSS

BAMLSS Prediction

Implicit Stochastic Gradient Descent Optimizer

Simulate longitudinal and survival data for joint models

smooth.construct

Constructor Functions for Smooth Terms in BAMLSS

smooth.construct.ms.smooth.spec

Smooth constructor for monotonic P-splines

Survival Model Transformer Function

Scaling Vectors and Matrices

Sampling Statistics

smooth.construct.sr.smooth.spec

Random Effects P-Spline

Stability selection.