growfunctions: Bayesian Non-Parametric Dependent Models for Time-Indexed Functional Data

Estimates a collection of time-indexed functions under either of Gaussian process (GP) or intrinsic Gaussian Markov random field (iGMRF) prior formulations where a Dirichlet process mixture allows sub-groupings of the functions to share the same covariance or precision parameters. The GP and iGMRF formulations both support any number of additive covariance or precision terms, respectively, expressing either or both of multiple trend and seasonality.

Version: 0.13
Depends: R (≥ 3.2.2), Rcpp (≥ 0.11.6)
Imports: Matrix (≥ 1.1), spam (≥ 0.41-0), mvtnorm (≥ 1.0-0), ggplot2 (≥ 1.0.1), reshape2 (≥ 1.2.2)
LinkingTo: Rcpp (≥ 0.11.6), RcppArmadillo (≥ 0.5.000)
Suggests: testthat (≥ 0.8.1)
Published: 2016-08-16
Author: Terrance Savitsky
Maintainer: Terrance Savitsky <tds151 at>
License: GPL (≥ 3)
NeedsCompilation: yes
Citation: growfunctions citation info
Materials: NEWS
In views: FunctionalData
CRAN checks: growfunctions results


Reference manual: growfunctions.pdf
Package source: growfunctions_0.13.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: growfunctions_0.13.tgz
OS X Mavericks binaries: r-oldrel: growfunctions_0.13.tgz
Old sources: growfunctions archive


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