Corrections requested by CRAN reviewer Benjamin Altmann:
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.bin_means()The bin_means() function signature has been
simplified.
within argument has been removed; the function now
always uses per-district maximum-likelihood estimation (the former
within = TRUE behavior).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).tol is now
1e-3 on the standardized scale, in place of the earlier
1e-6.bin_means() now returns components est_raw
and est_std in place of the earlier est_fc,
est_zero, and est_star.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.bin_means() now
returns for both the mean and the SD of such districts, matching the
methodological recommendation of Reardon et al. (2017).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.
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.mle_hetop() carried over from the HETOP 0.3-0
patchmle_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.
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).