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 |
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 El Capitan binaries: | r-release: btf_1.2.tgz |
OS X Mavericks binaries: | r-oldrel: btf_1.2.tgz |
Old sources: | btf archive |
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