Learn R Programming

growfunctions (version 0.17)

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

Description

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.

Copy Link

Version

Install

install.packages('growfunctions')

Monthly Downloads

442

Version

0.17

License

GPL (>= 3)

Maintainer

Terrance Savitsky

Last Published

November 14th, 2025

Functions in growfunctions (0.17)

growfunctions-package

Bayesian Non-Parametric Models for Estimating a Set of Denoised, Latent Functions From an Observed Collection of Domain-Indexed Time-Series

Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior

informative_plot

Plot credible intervals for parameters to compare ignoring with weighting an informative sample

Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior

Plot estimated functions, faceted by cluster numbers, for a known clustering

predict_functions.gmrfdpgrow

Use the model-estimated iGMRF precision parameters from gmrfdpgrow() to predict the iGMRF function at future time points. Inputs the gmrfdpgrow object of estimated parameters.

predict_functions.gpdpgrow

Use the model-estimated GP covariance parameters from gpdpgrow() to predict the GP function at future time points. Inputs the gpdpgrow object of estimated parameters.

Bayesian non-parametric dependent Gaussian process model for time-indexed functional data

predict_functions

Use the model-estimated covariance parameters from gpdpgrow() or gmrdpgrow to predict the function at future time points.

Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior

Produce samples of MCMC output

samples.gmrfdpgrow

Produce samples of MCMC output

Plot estimated functions both at estimated and predicted time points with 95% credible intervals.

Run a Bayesian functional data model under a GP prior with a fixed clustering structure that co-samples latent functions, bb_i.

Monthly employment counts from 1990 - 2013 from the Current Population Survey

gen_informative_sample

Generate a finite population and take an informative single or two-stage sample.

Compute normalized mean squared prediction error based on accuracy to impute missing data values

gmrfdpcountPost

Run a Bayesian functional data model under an instrinsic GMRF prior whose precision parameters employ a DP prior for a COUNT data response type where: y ~ poisson(E*exp(Psi)) Psi ~ N(gamma,tau_e^-1) which is a Poisson-lognormal model

Plot estimated functions for experimental units faceted by cluster versus data to assess fit.

Side-by-side plot panels that compare latent function values to data for different estimation models

Run a Bayesian functional data model under an instrinsic GMRF prior whose precision parameters employ a DP prior

Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior

Bayesian instrinsic Gaussian Markov Random Field model for dependent time-indexed functions