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: |
1.1 |
| 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 |
Downloads: