Description
Capability-Ecological Developmental Model (CEDM) Analysis.
Description
Implements the Capability-Ecological Developmental Model (CEDM) for longitudinal and multilevel data. The package supports estimation and interpretation of models examining how socioeconomic status (SES), health indicators, and contextual factors jointly relate to academic outcomes. Functionality includes: (1) classification of ecological capability regimes (amplifying, neutral, compensatory); (2) estimation of moderated multilevel models with higher-order interaction terms; (3) causal mediation analysis using doubly robust estimation; (4) random-effects within-between (REWB) decomposition; (5) nonlinear moderation using restricted cubic splines; (6) clustering of longitudinal health trajectories; and (7) sensitivity analysis using the impact threshold for a confounding variable (ITCV) and robustness-to-replacement (RIR) measures. The package is designed for use with general longitudinal multilevel datasets.
README.md
CEDMr
Overview
CEDMr implements the Capability-Ecological Developmental Model (CEDM) (Hait, 2025), a theoretical framework for studying how socioeconomic status (SES), health indicators (e.g., BMI), and ecological contexts jointly shape academic achievement across childhood.
The package operationalizes all five CEDM theoretical propositions into directly callable R functions and is designed to work with any longitudinal multi-level dataset, including ECLS-K:2011 and similar national surveys.
The Five CEDM Propositions
| # | Proposition | Function |
|---|---|---|
| 1 | Capability-Conversion Production Function — SES converts to achievement through health as a moderating capability constraint | cedm_production() |
| 2 | Ecological Regime Heterogeneity — Three regimes (amplifying, neutral, compensatory) govern the SES × health interaction | classify_regime(), cedm_production() |
| 3 | Developmental Recursion — Chronic between-child health differences (not transient fluctuations) drive achievement gaps | cedm_rewb(), cedm_trajectory() |
| 4 | Weak Mediation, Strong Moderation — Health mediates SES effects weakly but moderates them strongly | cedm_mediation() |
| 5 | Capability Stability — Robustness of SES–achievement effects to unmeasured confounding | cedm_sensitivity() |
Installation
# Install from GitHub
# install.packages("remotes")
remotes::install_github("causalfragility-lab/CEDMr")
Quick Start
Simulate CEDM data
library(CEDMr)
# Simulate 3000 observations across three ecological regimes
sim <- cedm_simulate(n = 3000, n_waves = 5, seed = 42)
table(sim$regime)
#>
#> amplifying compensatory neutral
#> 1000 1000 1000
Classify ecological capability regimes
sim <- classify_regime(sim, ses_var = "SES", ses_tertiles = TRUE)
table(sim$cedm_regime)
Fit the CEDM production function
# Base model: SES × health interaction
base_model <- cedm_production(
data = sim,
outcome_var = "achievement",
ses_var = "SES",
health_var = "health",
model = "base",
center = TRUE
)
print(base_model)
# Full regime model: SES × health × regime (three-way interaction)
regime_model <- cedm_production(
data = sim,
outcome_var = "achievement",
ses_var = "SES",
health_var = "health",
regime_var = "cedm_regime",
model = "regime",
center = TRUE
)
print(regime_model)
Causal mediation analysis (Proposition 4)
med <- cedm_mediation(
data = sim,
outcome_var = "achievement",
ses_var = "SES",
health_var = "health",
n_boot = 500,
seed = 42
)
print(med)
Within-between decomposition (Proposition 3)
rewb <- cedm_rewb(
data = sim,
outcome_var = "achievement",
health_var = "health",
id_var = "id",
time_var = "wave",
ses_var = "SES"
)
print(rewb)
Health trajectory clustering (Proposition 3)
traj <- cedm_trajectory(
data = sim,
health_var = "health",
id_var = "id",
time_var = "wave",
k = 3,
outcome_var = "achievement",
ses_var = "SES"
)
print(traj)
traj$plot
Sensitivity analysis (Proposition 5)
sens <- cedm_sensitivity(base_model, term = "SES")
print(sens)
Run the full pipeline in one call
pipeline <- cedm_full_pipeline(
data = sim,
outcome_var = "achievement",
ses_var = "SES",
health_var = "health",
id_var = "id",
time_var = "wave",
n_boot = 200,
seed = 42
)
print(pipeline)
pipeline$plots$regimes
pipeline$plots$interaction
Function Reference
| Function | Description |
|---|---|
cedm_simulate() | Simulate data from the CEDM data-generating process |
classify_regime() | Classify observations into amplifying / neutral / compensatory regimes |
cedm_production() | Fit the CEDM capability-conversion production function |
cedm_mediation() | Doubly robust causal mediation analysis |
cedm_rewb() | Random-effects within-between (REWB) decomposition |
cedm_trajectory() | Longitudinal health trajectory clustering |
cedm_spline_moderation() | Nonlinear moderation via restricted cubic splines |
cedm_sensitivity() | Sensitivity analysis using Frank's ITCV and RIR |
cedm_full_pipeline() | Run all CEDM analyses in a single call |
plot_regimes() | Plot SES × health scatter by ecological regime |
plot_cedm_interaction() | Plot predicted achievement by SES level and regime |
Citation
If you use CEDMr in your research, please cite:
Hait, S. (2026). Socioeconomic Status, Health, and Academic Achievement:
A Capability-Ecological Developmental Model. Manuscript under review.
Hait, S. (2026). CEDMr: Capability-Ecological Developmental Model (CEDM)
Analysis Toolkit. R package version 0.1.0.
https://github.com/causalfragility-lab/CEDMr
License
MIT © 2026 Subir Hait.