Package: MAPCtools 0.1.0

MAPCtools: Multivariate Age-Period-Cohort (MAPC) Modeling for Health Data

Bayesian multivariate age-period-cohort (MAPC) models for analyzing health data, with support for model fitting, visualization, stratification, and model comparison. Inference focuses on identifiable cross-strata differences, as described by Riebler and Held (2010) <doi:10.1093/biostatistics/kxp037>. Methods for handling complex survey data via the 'survey' package are included, as described in Mercer et al. (2014) <doi:10.1016/j.spasta.2013.12.001>.

Authors:Lars Vatten [aut, cre]

MAPCtools_0.1.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MAPCtools/json (API)

# Install 'MAPCtools' in R:

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

Bug tracker:https://github.com/larsvatten/mapctools/issues

Datasets:

toy_data - Synthetic Age-Period-Cohort Dataset

On CRAN:

3.70 score 1 stars 4 scripts 271 downloads 16 exports 83 dependencies

Last updated from:43b12afa80. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	208
source / vignettes	OK	263
linux-release-x86_64	OK	192
macos-release-arm64	OK	198
macos-oldrel-arm64	OK	220
windows-devel	OK	951
windows-release	OK	923
windows-oldrel	OK	171
wasm-release	OK	155

Exports:aggregate_df as.APC.df as.APC.NA.df fit_all_MAPC fit_MAPC generate_apc_lincombs generate_MAPC_formula plot_binned_counts plot_counts_1D plot_counts_2D plot_counts_with_mean plot_lincombs plot_mean_response_1D plot_mean_response_2D plot_missing_data validate_lincombs_against_formula

Dependencies:abind backports boot broom car carData cli colorspace corrplot cowplot cpp11 data.table DBI Deriv doBy dplyr farver fastDummies forecast Formula fracdiff generics ggplot2 ggpubr ggrepel ggsci ggsignif glue gridExtra gtable isoband labeling lattice lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels mgcv microbenchmark minqa mitools modelr nlme nloptr nnet numDeriv pbkrtest pillar pkgconfig polynom purrr quantreg R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas rlang rstatix S7 scales SparseM stringi stringr survey survival tibble tidyr tidyselect timeDate urca utf8 vctrs viridis viridisLite withr zoo

Quickstart: Exploratory Analysis & Model Fitting

Rendered fromquickstart.Rmdusingknitr::rmarkdown

Last update: 2025-06-25
Started: 2025-06-25

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Aggregate data across an entire data frame using sufficient statistics	aggregate_df
Add 1-indexed APC columns to data frame, handling numeric or categorical age/period	as.APC.df
Create NA structure across age, period and cohort groups based on strata	as.APC.NA.df
Fit all configurations of MAPC models using INLA	fit_all_MAPC
Fit a multivariable age-period-cohort model	fit_MAPC
Generate Age-Period-Cohort Linear Combinations for INLA	generate_apc_lincombs
Generate MAPC formula for INLA	generate_MAPC_formula
Plot counts of observations across bins of a numeric variable, optionally stratified	plot_binned_counts
Plot counts of observations across a single variable, optionally stratified	plot_counts_1D
Plot counts of observations across two dimensions, optionally stratified	plot_counts_2D
Plot heatmap of observation counts with mean overlay, optionally stratified	plot_counts_with_mean
Plot Linear Combinations of Age-Period-Cohort Effects by Strata	plot_lincombs
Plot mean of a response variable across a single variable, optionally stratified	plot_mean_response_1D
Plot mean of a response variable across two dimensions, optionally stratified	plot_mean_response_2D
Plot Missing Group Combinations	plot_missing_data
Synthetic Age-Period-Cohort Dataset	toy_data
Validate lincomb terms against an INLA formula	validate_lincombs_against_formula