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: | 1.1 |
| Depends: | R (≥ 3.1.0) |
| Imports: | Matrix, coda |
| LinkingTo: | Rcpp (≥ 0.11.0), RcppEigen (≥ 0.3.2.1.1) |
| Published: | 2014-07-30 |
| Author: | Edward A. Roualdes |
| Maintainer: | Edward A. Roualdes <edward.roualdes at uky.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)] |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | btf results |
| Reference manual: | btf.pdf |
| Package source: | btf_1.1.tar.gz |
| Windows binaries: | r-devel: btf_1.1.zip, r-release: btf_1.1.zip, r-oldrel: btf_1.1.zip |
| OS X Mavericks binaries: | r-release: btf_1.1.tgz, r-oldrel: btf_1.1.tgz |
| Old sources: | btf archive |
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