Multivariate Age-Period-Cohort (MAPC) Modeling for Health Data
Aggregate binomial data
Add 1-indexed age, period and cohort indices via match()
Add cohort column to data frame
Add cohort column to data frame
Aggregate Gaussian data
Aggregate data across an entire data frame using sufficient statistics
Aggregate grouped data using aggregate_df
Aggregate multinomial data. Used in aggregate_df.
Add 1-indexed APC columns to data frame, handling numeric or categoric...
Create NA structure across age, period and cohort groups based on stra...
Aggregate binomial data
Check if a set of columns is missing from a data frame. For use in `ag...
Clamp a numeric value within bounds
Compute dynamic pretty breaks for continuous x-axis
Compute dynamic pretty breaks for discrete x-axis
Find expected groups based on distinct values across a set of variable...
Fit all configurations of MAPC models using INLA
Fit a multivariable age-period-cohort model
Aggregate Gaussian data
Generate Age-Period-Cohort Linear Combinations for INLA
Generate MAPC formula for INLA
Function for finding longest consecutive run of non-missing indices
Make a table of model fit scores
Aggregate multinomial data (No changes needed here)
Count number of groups across a set of variables in a data frame
Count number of groups across a set of variables in a data frame
Plot counts of observations across bins of a numeric variable, optiona...
Plot counts of observations across a single variable, optionally strat...
Plot counts of observations across two dimensions, optionally stratifi...
Plot heatmap of observation counts with mean overlay, optionally strat...
Plot Linear Combinations of Age-Period-Cohort Effects by Strata
Plot mean of a response variable across a single variable, optionally ...
Plot mean of a response variable across two dimensions, optionally str...
Plot Missing Group Combinations
Make a plot of model fit scores
Plot time effects with uncertainty ribbons
Helper for evaluating column names, strings, or self-contained vectors
Validate lincomb terms against an INLA formula
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>.