bamlss v1.1-2

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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 in bamlss

Name Description
DIC Deviance Information Criterion
coef.bamlss Extract BAMLSS Coefficients
WAIC Watanabe-Akaike Information Criterion (WAIC)
continue Continue Sampling
bamlss.frame Create a Model Frame for BAMLSS
colorlegend Plot a Color Legend
bamlss-package Bayesian Additive Models for Location Scale and Shape (and Beyond)
cox_mcmc Cox Model Markov Chain Monte Carlo
Austria Austria States and Topography
BayesX Markov Chain Monte Carlo for BAMLSS using BayesX
GAMart GAM Artificial Data Set
Volcano Artificial Data Set based on Auckland's Maunga Whau Volcano
boost Boosting BAMLSS
fitted.bamlss BAMLSS Fitted Values
c95 Compute 95% Credible Interval and Mean
gF Get a BAMLSS Family
bamlss Fit Bayesian Additive Models for Location Scale and Shape (and Beyond)
lin Linear Effects for BAMLSS
Surv2 Create a Survival Object for Joint Models
bboost Bootstrap Boosting
bfit Fit BAMLSS with Backfitting
smooth.construct.kr.smooth.spec Kriging Smooth Constructor
cox_mode Cox Model Posterior Mode Estimation
LondonFire London Fire Data
MVNORM Create Samples for BAMLSS by Multivariate Normal Approximation
bamlss.formula Formulae for BAMLSS
dl.bamlss Deep Learning BAMLSS
la Lasso Smooth Constructor
family.bamlss Distribution Families in bamlss
GMCMC General Markov Chain Monte Carlo for BAMLSS
bamlss.engine.helpers BAMLSS Engine Helper Functions
model.frame.bamlss BAMLSS Model Frame
plot2d Plot 2D Effects
jm_bamlss Fit Flexible Additive Joint Models
plot.bamlss Plotting BAMLSS
parameters Extract or Initialize Parameters for BAMLSS
bamlss.engine.setup BAMLSS Engine Setup Function
pathplot Plot Coefficients Paths
results.bamlss.default Compute BAMLSS Results for Plotting and Summaries
plotmap Plot Maps
rmf Remove Special Characters
cox_predict Cox Model Prediction
homstart_data HOMSTART Precipitation Data
n Neural Networks for BAMLSS
neighbormatrix Compute a Neighborhood Matrix from Spatial Polygons
plot3d Plot 3D Effects
residuals.bamlss Compute BAMLSS Residuals
smooth.construct Constructor Functions for Smooth Terms in BAMLSS
isgd Implicit Stochastic Gradient Descent Optimizer
plotblock Factor Variable and Random Effects Plots
model.matrix.bamlss.frame Construct/Extract BAMLSS Design Matrices
s2 Special Smooths in BAMLSS Formulae
rb Random Bits for BAMLSS
smooth.construct.ms.smooth.spec Smooth constructor for monotonic P-splines
samples Extract Samples
simSurv Simulate Survival Times
randomize Transform Smooth Constructs to Random Effects
smooth_check MCMC Based Simple Significance Check for Smooth Terms
terms.bamlss BAMLSS Model Terms
predict.bamlss BAMLSS Prediction
summary.bamlss Summary for BAMLSS
stabsel Stability selection.
smooth.construct.sr.smooth.spec Random Effects P-Spline
response_name Extract the reponse name of a bamlss.frame object.
sliceplot Plot Slices of Bivariate Functions
boost2 Some Shortcuts
samplestats Sampling Statistics
surv_transform Survival Model Transformer Function
simJM Simulate longitudinal and survival data for joint models
scale2 Scaling Vectors and Matrices
Golf Prices of Used Cars Data
JAGS Markov Chain Monte Carlo for BAMLSS using JAGS
Crazy Crazy simulated data
bbfit Batchwise Backfitting
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Vignettes of bamlss

Name
bamlss.Rmd
bamlss.bib
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Last month downloads

Details

Date 2020-02-19
License GPL-2 | GPL-3
LazyLoad yes
RoxygenNote 6.1.1
URL http://www.bamlss.org/
VignetteBuilder knitr
NeedsCompilation yes
Packaged 2020-02-19 21:33:19 UTC; nik
Repository CRAN
Date/Publication 2020-02-19 22:20:03 UTC

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