achieveGap
0.1.0Modeling Achievement Gap Trajectories with Hierarchical Penalized Splines
Overview
Implements a hierarchical penalized spline framework for estimating achievement gap trajectories in longitudinal educational data. The achievement gap between two groups (e.g., low versus high socioeconomic status) is modeled directly as a smooth function of grade while the baseline trajectory is estimated simultaneously within a mixed-effects model. Smoothing parameters are selected using restricted maximum likelihood (REML), and simultaneous confidence bands with correct joint coverage are constructed using posterior simulation. The package also includes functions for simulation-based benchmarking, visualization of gap trajectories, and hypothesis testing for global and grade-specific differences. The modeling framework builds on penalized spline methods (Eilers and Marx, 1996, doi:10.1214/ss/1038425655) and generalized additive modeling approaches (Wood, 2017, doi:10.1201/9781315370279), with uncertainty quantification following Marra and Wood (2012, doi:10.1111/j.1467-9469.2011.00760.x).
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- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-205 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 33%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 9%
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Checks run against github.com/causalfragility-lab/achievegap on 2026-07-31.
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Code & Tests
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.0Latest2026-03-19 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-03-19
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.1.0
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet