bamlss v0.1-2

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by Nikolaus Umlauf

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 (2017) <http://EconPapers.RePEc.org/RePEc:inn:wpaper:2017-05>.

Functions in bamlss

Name Description
GMCMC
JAGS
bamlss.formula
bamlss.frame
la Lasso Smooth Constructor
model.frame.bamlss
rmf
s2
simSurv
sliceplot
DIC
GAMart GAM Artificial Data Set
bfit
boost Gradient Boosting BAMLSS
cox.predict
family.bamlss
residuals.bamlss
results.bamlss.default
LondonFire London Fire Data
MVNORM
c95
coef.bamlss
plotblock
plotmap
scale2
simJM
smooth.construct Constructor Functions for Smooth Terms in BAMLSS
summary.bamlss
Austria Austria States and Topography
BayesX
colorlegend
continue
model.matrix.bamlss.frame
neighbormatrix
cox.mcmc
cox.mode
parameters Extract or Initialize Parameters for BAMLSS
plot.bamlss
samples
samplestats
bamlss-package
bamlss
homstart_data
jm_bamlss
plot2d
plot3d
surv.transform
terms.bamlss
bamlss.engine.helpers
bamlss.engine.setup
Surv2
Volcano
fitted.bamlss
gF
randomize
predict.bamlss
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Details

Date 2017-04-13
License GPL-2 | GPL-3
LazyLoad yes
NeedsCompilation yes
Packaged 2017-04-14 03:24:01 UTC; nik
Repository CRAN
Date/Publication 2017-04-14 14:05:48 UTC

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