Package: growfunctions 0.16

Terrance Savitsky

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

growfunctions_0.16.tar.gz
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growfunctions.pdf |growfunctions.html✨
growfunctions/json (API)
NEWS

# Install 'growfunctions' in R:
install.packages('growfunctions', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • cps - Monthly employment counts from 1990 - 2013 from the Current Population Survey

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 321 downloads 18 exports 36 dependencies

Last updated 12 months agofrom:fa564a1d37. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 07 2024
R-4.5-linux-x86_64OKNov 07 2024

Exports:cluster_plotfit_comparegen_informative_samplegmrfdpcountPostgmrfdpgrowgmrfdpPostgpBFixPostgpdpbPostgpdpgrowgpdpPostgpFixPostgpPostinformative_plotMSPEplot_clusterpredict_functionspredict_plotsamples

Dependencies:clicolorspacedotCall64fansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangscalesspamstringistringrtibbleutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Bayesian Non-Parametric Models for Estimating a Set of Denoised, Latent Functions From an Observed Collection of Domain-Indexed Time-Seriesgrowfunctions-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 Surveycps
Side-by-side plot panels that compare latent function values to data for different estimation modelsfit_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 modelgmrfdpcountPost
Bayesian instrinsic Gaussian Markov Random Field model for dependent time-indexed functionsgmrfdpgrow gmrfdpgrow.default
Run a Bayesian functional data model under an instrinsic GMRF prior whose precision parameters employ a DP priorgmrfdpPost
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 priorgpdpbPost
Bayesian non-parametric dependent Gaussian process model for time-indexed functional datagpdpgrow gpdpgrow.default
Run a Bayesian functional data model under a GP prior whose parameters employ a DP priorgpdpPost
Run a Bayesian functional data model under a GP prior whose parameters employ a DP priorgpFixPost
Run a Bayesian functional data model under a GP prior whose parameters employ a DP priorgpPost
Plot credible intervals for parameters to compare ignoring with weighting an informative sampleinformative_plot
Compute normalized mean squared prediction error based on accuracy to impute missing data valuesMSPE
Plot estimated functions, faceted by cluster numbers, for a known clusteringplot_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 outputsamples samples.gpdpgrow
Produce samples of MCMC outputsamples.gmrfdpgrow