Package index • lsastrat

Estimators

First-class stratification and inequality quantities.

social_gradient(): Socio-economic achievement gradient
segregation_index(): School segregation indices for large-scale assessment data
ieop(): Inequality of educational opportunity (Ferreira-Gignoux)
variance_decomposition(): Between- and within-school variance decomposition

Fit arbitrary models, and compare across cycles.

lsa_model(): Fit any model to plausible-value outcomes with replicate-weight inference
lsa_trend(): Trend across assessment cycles with linking error

Attributing inequality to its sources.

ieop_decompose(): Shapley decomposition of inequality of educational opportunity
oaxaca_gap(): Oaxaca-Blinder decomposition of an achievement gap

Comparative and robustness analysis.

lsa_by(): Run an estimator across groups (e.g. countries)
equity_scatter(): Quality-equity scatter across groups
lsa_multiverse(): Plausible-value / weight-aware multiverse (specification-curve) analysis
lsa_design(): Bundle a survey design for reuse

The shared plausible-value pooling and replicate-weight machinery.

pool_pv(): Pool point estimates and variances across plausible values (Rubin's rules)
rep_factor(): Replicate-weight variance factors

Accessors and plots for fitted objects.

print(<lsastrat_estimate>) summary(<lsastrat_estimate>) coef(<lsastrat_estimate>) confint(<lsastrat_estimate>) as.data.frame(<lsastrat_estimate>): Methods for lsastrat estimate objects
tidy() glance(): Tidy an lsastrat estimate
autoplot(<lsastrat_estimate>) autoplot(<social_gradient>) autoplot(<lsastrat_by>) autoplot(<equity_scatter>) autoplot(<lsa_multiverse>): ggplot2 autoplot methods
plot(<social_gradient>): Plot a socio-economic achievement gradient
plot(<segregation>): Plot a school segregation profile
plot(<lsastrat_by>): Forest / caterpillar plot of an estimate across groups
plot(<equity_scatter>): Plot a quality-equity scatter
plot(<lsa_multiverse>): Specification-curve plot
plot(<lsa_trend>): Plot a cross-cycle trend