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.

Version: 1.2
Depends: R (≥ 3.1.0)
Imports: Rcpp (≥ 0.12.0), Matrix, coda
LinkingTo: Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.2.2.0)
Suggests: knitr
Published: 2017-05-31
Author: Edward A. Roualdes
Maintainer: Edward A. Roualdes <eroualdes at csuchico.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
NeedsCompilation: yes
Materials: README
CRAN checks: btf results

Downloads:

Reference manual: btf.pdf
Vignettes: btf
Package source: btf_1.2.tar.gz
Windows binaries: r-devel: btf_1.2.zip, r-release: btf_1.2.zip, r-oldrel: btf_1.2.zip
OS X binaries: r-release: btf_1.2.tgz, r-oldrel: btf_1.2.tgz
Old sources: btf archive

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