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.

Author
Edward A. Roualdes
Date of publication
2014-07-30 16:15:16
Maintainer
Edward A. Roualdes <edward.roualdes@uky.edu>
License
GPL (>= 2.0)
Version
1.1

View on CRAN

Man pages

btf
Bayesian trend filtering via Eigen
genDelta
generate Matrix D^k+1
getPost
get posterior draws from btf object
getPostEst
get posterior estimates from btf object
plot.btf
plot btf object
tf
approximate trend filtering via MM algorithm

Files in this package

btf
btf/src
btf/src/Makevars
btf/src/gdPBTF.cpp
btf/src/dexpBTF.cpp
btf/src/tf_approx.cpp
btf/src/individual.cpp
btf/src/Makevars.win
btf/src/RcppExports.cpp
btf/NAMESPACE
btf/R
btf/R/plot.btf.R
btf/R/delta.R
btf/R/RcppExports.R
btf/R/posterior.R
btf/R/tf.R
btf/R/btf.R
btf/R/util.R
btf/README.md
btf/MD5
btf/DESCRIPTION
btf/man
btf/man/genDelta.Rd
btf/man/plot.btf.Rd
btf/man/btf.Rd
btf/man/getPostEst.Rd
btf/man/tf.Rd
btf/man/getPost.Rd