binest 0.2-1 (resubmission)

Corrections requested by CRAN reviewer Benjamin Altmann:

• DESCRIPTION: function names now written with parentheses (e.g. bin_means()); package and software names (‘HETOP’, ‘CRAN’) now in single quotes per CRAN style.
• man/waic_hetop.Rd: replaced commented-out example with a runnable example wrapped in \donttest{}.
• R/fh_hetop.R: suppressed JAGS’ default console output. The progress bar now defaults to "none" rather than "text", and the R2jags::jags() call is wrapped in capture.output() so JAGS’ C++ initialization messages do not write to stdout.

binest 0.2-0 (2026-05-29)

API changes to bin_means()

The bin_means() function signature has been simplified.

• The within argument has been removed; the function now always uses per-district maximum-likelihood estimation (the former within = TRUE behavior).
• The sampling_var argument has been removed; empirical-Bayes shrinkage always uses the Fisher information from the binned-normal likelihood (the former sampling_var = "fisher" behavior).
• The default convergence tolerance tol is now 1e-3 on the standardized scale, in place of the earlier 1e-6.

Output structure

• bin_means() now returns components est_raw and est_std in place of the earlier est_fc, est_zero, and est_star.
• The per-group estimates inside est_raw and est_std are named group_mean_mle and group_sd_mle when eb_shrink = FALSE, and group_mean_eb and group_sd_eb when eb_shrink = TRUE, in place of the earlier mug and sigmag.
• bin_means() now also returns a per-group gof data frame with the chi-square statistic, degrees of freedom, and p-value of a goodness-of-fit test for the within-group normality assumption.

Cleaner unidentified-district handling

• Districts with fewer than three populated bins are not jointly identified under within-group normality. bin_means() now returns for both the mean and the SD of such districts, matching the methodological recommendation of Reardon et al. (2017).

binest 0.1-0 (2026-05-27)

Renamed from HETOP

This package was previously called HETOP, originally authored and maintained by J.R. Lockwood (last CRAN release: HETOP 0.2-6, June 2019; archived from CRAN in March 2025). It is renamed binest to reflect the broader scope of methods now included for estimating distributional moments from binned (coarsened) count data.

New estimator

• bin_means() — a fast per-group estimator of means and SDs under within-group normality. Uses the pooled bin proportions to derive standardized cutpoints, then weights truncated-normal moments within each bin by within-group bin proportions. Returns estimates on the same four scales as mle_hetop(). Runs in O(GK) time.

Bug fix in mle_hetop() carried over from the HETOP 0.3-0 patch

mle_hetop() previously took two user-facing arguments, fixedcuts and svals. Internal starting values for group means and log SDs are on a standardized scale; when the supplied cutpoints were on a native test-score scale (e.g. STAAR scale scores like 1536 and 1653), the log likelihood was numerically flat near the starting values, nlm() exited with zero iterations, and the function silently returned the starting values disguised as MLE estimates.

mle_hetop() no longer accepts fixedcuts or svals. Cutpoints are derived internally from the pooled bin proportions via

qnorm(cumsum(colSums(ngk)/sum(ngk))[1:2])

so the cutpoints, starting means, and starting log SDs all live on the same standardized scale. The function warns if nlm() reports zero iterations. The cell-probability computation is also vectorized for roughly an 18x speedup per likelihood evaluation.

The bug was first identified by David J. Hunter in January 2022 and independently reproduced by Benjamin R. Shear in July 2024.

History of HETOP

The HETOP package was originally authored by J.R. Lockwood, with substantial methodological contributions from Sean F. Reardon, Benjamin R. Shear, Katherine E. Castellano, and Andrew D. Ho. The implementations of fh_hetop, gendata_hetop, triple_goal, and waic_hetop in this package are unchanged from HETOP 0.2-6 (J.R. Lockwood, June 2019).