Package: growfunctions 0.16

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.

Authors:Terrance Savitsky

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growfunctions/json (API)
NEWS

# Install 'growfunctions' in R:

install.packages('growfunctions', repos = c('https://tds151.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

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

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.75 score 56 scripts 347 downloads 18 exports 36 dependencies

Last updated 11 months agofrom:fa564a1d37. Checks:OK: 9. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 08 2024
R-4.5-win-x86_64	OK	Oct 08 2024
R-4.5-linux-x86_64	OK	Oct 08 2024
R-4.4-win-x86_64	OK	Oct 08 2024
R-4.4-mac-x86_64	OK	Oct 08 2024
R-4.4-mac-aarch64	OK	Oct 08 2024
R-4.3-win-x86_64	OK	Oct 08 2024
R-4.3-mac-x86_64	OK	Oct 08 2024
R-4.3-mac-aarch64	OK	Oct 08 2024

Exports:cluster_plot fit_compare gen_informative_sample gmrfdpcountPost gmrfdpgrow gmrfdpPost gpBFixPost gpdpbPost gpdpgrow gpdpPost gpFixPost gpPost informative_plot MSPE plot_cluster predict_functions predict_plot samples

Dependencies:cli colorspace dotCall64 fansi farver ggplot2 glue gtable isoband labeling lattice lifecycle magrittr MASS Matrix mgcv munsell nlme pillar pkgconfig plyr R6 RColorBrewer Rcpp RcppArmadillo reshape2 rlang scales spam stringi stringr tibble utf8 vctrs viridisLite withr

Help page	Topics
Bayesian Non-Parametric Models for Estimating a Set of Denoised, Latent Functions From an Observed Collection of Domain-Indexed Time-Series	growfunctions-package growfunctions package-growfunctions
Plot estimated functions for experimental units faceted by cluster versus data to assess fit.	cluster_plot
Monthly employment counts from 1990 - 2013 from the Current Population Survey	cps
Side-by-side plot panels that compare latent function values to data for different estimation models	fit_compare
Generate a finite population and take an informative single or two-stage sample.	gen_informative_sample
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	gmrfdpcountPost
Bayesian instrinsic Gaussian Markov Random Field model for dependent time-indexed functions	gmrfdpgrow gmrfdpgrow.default
Run a Bayesian functional data model under an instrinsic GMRF prior whose precision parameters employ a DP prior	gmrfdpPost
Run a Bayesian functional data model under a GP prior with a fixed clustering structure that co-samples latent functions, 'bb_i'.	gpBFixPost
Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior	gpdpbPost
Bayesian non-parametric dependent Gaussian process model for time-indexed functional data	gpdpgrow gpdpgrow.default
Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior	gpdpPost
Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior	gpFixPost
Run a Bayesian functional data model under a GP prior whose parameters employ a DP prior	gpPost
Plot credible intervals for parameters to compare ignoring with weighting an informative sample	informative_plot
Compute normalized mean squared prediction error based on accuracy to impute missing data values	MSPE
Plot estimated functions, faceted by cluster numbers, for a known clustering	plot_cluster
Use the model-estimated covariance parameters from gpdpgrow() or gmrdpgrow to predict the function at future time points.	predict_functions
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.gmrfdpgrow
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.	predict_functions.gpdpgrow
Plot estimated functions both at estimated and predicted time points with 95% credible intervals.	predict_plot
Produce samples of MCMC output	samples samples.gpdpgrow
Produce samples of MCMC output	samples.gmrfdpgrow

Package: growfunctions 0.16

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

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