This package is considered a duplicate. The official version of this package is found at:https://accidda.r-universe.dev/imuGAP

Package: imuGAP 0.2.0

Carl Pearson

imuGAP: Immunity: Geographic and Age-Based Projection

Fits Bayesian hierarchical models of vaccine coverage by location, birth cohort, and age. Observations of vaccination status (which may span cohorts, ages, and doses) are used to fit a latent survival-style process model that decomposes coverage into a lifetime propensity to vaccinate and a time-varying force of vaccination. Hierarchical spatial structure (e.g., state, county, school) supports partial pooling via random effects. Models are implemented in 'Stan' and fit via 'rstan'. Provides helpers to validate user-supplied input data, and to predict coverage from fitted models.

Authors:Carl Pearson [aut, cre], Claire Perrin Smith [aut], Kelly Zhen [ctb], Weston Voglesonger [ctb], Joshua Chen [ctb], Minjae Kung [ctb]

imuGAP_0.2.0.tar.gz
imuGAP_0.2.0.zip(r-4.7-x86_64)imuGAP_0.2.0.zip(r-4.7-arm64)imuGAP_0.2.0.zip(r-4.6-x86_64)imuGAP_0.2.0.zip(r-4.6-arm64)
imuGAP_0.2.0.tar.gz(r-4.7-arm64)imuGAP_0.2.0.tar.gz(r-4.7-x86_64)imuGAP_0.2.0.tar.gz(r-4.6-arm64)imuGAP_0.2.0.tar.gz(r-4.6-x86_64)
imuGAP_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
imuGAP/json (API)

# Install 'imuGAP' in R:

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

Bug tracker:https://github.com/accidda/imugap/issues

Pkgdown/docs site:https://accidda.github.io

Uses libs:

onetbb– Parallelism library for C++
c++– GNU Standard C++ Library v3

Datasets:

fit_sim - Example Stan Fit
fit_sim_1layer - Example Stan Fit
fit_sim_2layer - Example Stan Fit
latent_params_sim - Example Latent Parameter Values
locations_sim - Example Location Data
observations_sim - Example Observation Data
populations_sim - Example Population Data
predict_sim - Example Coverage Predictions
predict_sim_1layer - Example Coverage Predictions
predict_sim_2layer - Example Coverage Predictions
target_sim - Example Prediction Target Populations
target_sim_1layer - Example Prediction Target Populations
target_sim_2layer - Example Prediction Target Populations

On CRAN:

onetbb cpp

3.38 score 12 scripts 224 downloads 10 exports 51 dependencies

Last updated from:850a5c8791. Checks:10 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	295
linux-devel-x86_64	OK	378
source / vignettes	OK	506
linux-release-arm64	OK	332
linux-release-x86_64	OK	339
windows-devel-arm64	OK	441
windows-devel-x86_64	OK	386
windows-release-arm64	OK	412
windows-release-x86_64	OK	338
wasm-release	OK	251

Exports:canonicalize_locations canonicalize_observations canonicalize_populations canonicalize_target create_observation_populations create_target extract_imugap imugap_options sampling stan_options

Dependencies:abind backports BH callr checkmate cli cpp11 data.table desc distributional farver flexstanr generics ggplot2 glue gridExtra gtable inline isoband labeling lifecycle loo magrittr matrixStats numDeriv otel parallelly pillar pkgbuild pkgconfig posterior processx ps QuickJSR R6 RColorBrewer Rcpp RcppEigen RcppParallel rlang rstan rstantools S7 scales StanHeaders tensorA tibble utf8 vctrs viridisLite withr

Flexible Location Layers in imuGAP

Rendered fromuser_specified_layers.Rmdusingknitr::rmarkdown

Last update: 2026-09-02
Started: 2026-09-02

imuGAP, Immunity: Geographic & Age-based Projection

Introduction | Walkthrough of Basic Usage | 1. Preparing and Validating the Input Data | Location Hierarchy (locations_sim) | Coverage Observations (observations_sim) | Observation Metadata (populations_sim) | Validation Failure Examples | 2. Exploring the Synthetic Dataset and Latent Features | State-Level Observations & Latent Propensity | County-Level Observations & Offsets | School-Level Observations Across Counties | 3. Fitting the Model | Basis Spline Coefficients ($\beta_{\text{bs}}$) | Hierarchy Layer Variances ($\sigma$) | Vaccination Uptake Rates ($\lambda_{\text{raw}}$) | 4. Defining a Target for Predictions | 5. Predicting Coverage

Rendered fromimuGAP.Rmdusingknitr::rmarkdown

Last update: 2026-09-02
Started: 2026-06-22

Help page	Topics
The 'imuGAP' package.	imuGAP-package imuGAP
Convert coverage predictions to a data.frame	as.data.frame.imugap_predict
Canonicalize imuGAP Data Objects	canonicalize canonicalize_locations canonicalize_observations canonicalize_populations
Canonicalize a target grid against a fit	canonicalize_target
Create observation populations	create_observation_populations
Construct a target grid for prediction	create_target
Custom imuGAP fit extraction	extract_imugap
Example Stan Fit	fit_sim
Example Stan Fit (1 Layer)	fit_sim_1layer
Example Stan Fit (2 Layers)	fit_sim_2layer
imuGAP Model Options	imugap_options
Example Latent Parameter Values	latent_params_sim
Example Location Data	locations_sim
Example Observation Data	observations_sim
Example Population Data	populations_sim
Example Coverage Predictions	predict_sim
Example Coverage Predictions (1 Layer)	predict_sim_1layer
Example Coverage Predictions (2 Layers)	predict_sim_2layer
Predict coverage probabilities	predict.imugap_fit
Immunity: Geographic & Age-based Projection, 'imuGAP'	sampling
Subset coverage predictions	subset.imugap_predict
Summarize coverage predictions	summary.imugap_predict
Example Prediction Target Populations	target_sim
Example Prediction Target Populations (1 Layer)	target_sim_1layer
Example Prediction Target Populations (2 Layers)	target_sim_2layer

Package: imuGAP 0.2.0

imuGAP: Immunity: Geographic and Age-Based Projection

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)