binest

Estimation of group-level means and standard deviations from binned (coarsened) count data. The package implements three methods with a common output structure:

• bin_means() — fast per-group estimator under within-group normality. Linear in the number of groups times the number of bins.
• mle_hetop() — maximum-likelihood fit of the heteroskedastic ordered probit (HETOP) model.
• fh_hetop() — Bayesian variant of HETOP via MCMC.

This package was previously called HETOP and was maintained by J. R. Lockwood; it is renamed and extended to reflect the broader set of estimators now included.

Installation

# install.packages("remotes")
remotes::install_github("paulvonhippel/binest")

(Or install.packages("binest") once on CRAN.)

A quick example

library(binest)
data(tx_g6_math_2018)

ngk <- with(tx_g6_math_2018,
            cbind(unsatisfactory, approaches, meets, masters))
cuts <- c(1536, 1653, 1772)

fit <- bin_means(ngk, cutpoints = cuts)
cor(fit$est_raw$group_mean_mle, tx_g6_math_2018$reported_mean)

See vignette("binest") for a full comparison of the three estimators on the Texas STAAR Grade-6 mathematics data.

References

• Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London A, 222, 309-368.
• Lockwood, J. R., Castellano, K. E., & Shear, B. R. (2018). Flexible Bayesian models for inferences from coarsened, group-level achievement data. JEBS, 43(6), 663-692.
• Reardon, S. F., Shear, B. R., Castellano, K. E., & Ho, A. D. (2017). Using heteroskedastic ordered probit models to recover moments of continuous test score distributions from coarsened data. JEBS, 42(1), 3-45.
• Sheppard, W. F. (1898). On the calculation of the most probable values of frequency-constants for data arranged according to equidistant divisions of a scale. PLMS, 29, 353-380.

License

GPL (>= 2).