Routines for Descriptive and Model-Based APC Analysis
Overview
Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments. The 'APCtools' package offers visualization techniques and general routines to simplify the workflow of an APC analysis. Sophisticated functions are available both for descriptive and regression model-based analyses. For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization techniques building on the concept of Lexis diagrams. Model-based analyses build on the separation of the temporal dimensions based on generalized additive models, where a tensor product interaction surface (usually between age and period) is utilized to represent the third dimension (usually cohort) on its diagonal. Such tensor product surfaces can also be estimated while accounting for further covariates in the regression model. See Weigert et al. (2021) doi:10.1177/1354816620987198 for methodological details.
- updated some ggplot2 dependencies in the plot_density functions to prevent the use of now deprecated ggplot2 arguments
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- Help pages
- 29
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- 100%12 run in a plain check
- Exported functions with examples
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- Documented parameters
- 90%
- Return-value docs
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- Help pages citing references
- 24%
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Package metadata
- First published
- 2022-01-05
- Total releases
- 4 / 4 yrs
- License
- MIT + file LICENSE OSI · relicensed from GPL-3 in 1.0.1
- Minimum R
- ≥ 3.5
- Bundled data
- 253 KB / 2 files
- Download size
- 3.7 MB
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