Package: binest 0.2-1

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.

Authors:Paul T. von Hippel [aut, cre], David J. Hunter [aut], J.R. Lockwood [aut]