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.

AuthorEdward A. Roualdes
Date of publication2014-07-30 16:15:16
MaintainerEdward A. Roualdes <edward.roualdes@uky.edu>
LicenseGPL (>= 2.0)
Version1.1

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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

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