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binest  

Estimation of Group Means and SDs from Binned Count Data
View on CRAN: Click here


Download and install binest package within the R console
Install from CRAN:
install.packages("binest")

Install from Github:
library("remotes")
install_github("cran/binest")

Install by package version:
library("remotes")
install_version("binest", "0.2-1")



Attach the package and use:
library("binest")
Maintained by
Paul T. von Hippel
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-08
Latest Update: 2026-06-08
Description:
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.
How to cite:
Paul T. von Hippel (2026). binest: Estimation of Group Means and SDs from Binned Count Data. R package version 0.2-1, https://cran.r-project.org/web/packages/binest. Accessed 13 Sep. 2026.
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