btf: Estimates univariate function via Bayesian trend filtering
Version 1.1

Trend filtering uses the generalized lasso framework to fit an adaptive polynomial of degree k to estimate the function f_0 at each input x_i in the model: y_i = f_0(x_i) + epsilon_i, for i = 1, ..., n, and epsilon_i is sub-Gaussian with E(epsilon_i) = 0. Bayesian trend filtering adapts the genlasso framework to a fully Bayesian hierarchical model, estimating the penalty parameter lambda within a tractable Gibbs sampler.

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AuthorEdward A. Roualdes
Date of publication2014-07-30 16:15:16
MaintainerEdward A. Roualdes <edward.roualdes@uky.edu>
LicenseGPL (>= 2.0)
Version1.1
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("btf")

Man pages

btf: Bayesian trend filtering via Eigen
genDelta: generate Matrix D^k+1
getPost: get posterior draws from btf object
getPostEst: get posterior estimates from btf object
plot.btf: plot btf object
tf: approximate trend filtering via MM algorithm

Functions

btf Man page Source code
dexpBTF Source code
gdPBTF Source code
genDelta Man page Source code
getPost Man page Source code
getPostEst Man page Source code
plot.btf Man page Source code
tf Man page Source code
tf_approx Source code

Files

src
src/Makevars
src/gdPBTF.cpp
src/dexpBTF.cpp
src/tf_approx.cpp
src/individual.cpp
src/Makevars.win
src/RcppExports.cpp
NAMESPACE
R
R/plot.btf.R
R/delta.R
R/RcppExports.R
R/posterior.R
R/tf.R
R/btf.R
R/util.R
README.md
MD5
DESCRIPTION
man
man/genDelta.Rd
man/plot.btf.Rd
man/btf.Rd
man/getPostEst.Rd
man/tf.Rd
man/getPost.Rd
btf documentation built on May 19, 2017, 11:46 p.m.