binest: Estimation of Group Means and SDs from Binned Count Data
Estimates group-level means and standard deviations from
binned (coarsened) count data, where the within-bin scores are
unobserved. The package implements three methods that share a
common output structure: bin_means() (a fast estimator that
assumes within-district normality and uses pooled bin proportions
to derive bin-conditional truncated-normal expectations),
mle_hetop() (maximum likelihood for the heteroskedastic ordered
probit model of Reardon, Shear, Castellano and Ho 2017
<doi:10.3102/1076998616666279>), and fh_hetop() (the Bayesian
Fay-Herriot variant of Lockwood, Castellano and Shear 2018
<doi:10.3102/1076998618795124>). The mle_hetop() and fh_hetop()
functions are forked from the 'HETOP' package by J. R. Lockwood
('CRAN', last released 2019). mle_hetop() has been modified to
speed up the runtime via a vectorized inner loop and to remove
two user-facing arguments (fixedcuts and svals) that some users
found confusing; cutpoints and starting values are now derived
internally from the data.
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