btf: Estimates univariate function via Bayesian trend filtering

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.

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

View on CRAN

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

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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