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grassr
View on CRAN: Click
here
Download and install grassr package within the R console
Install from CRAN:
install.packages("grassr")
Install from Github:
library("remotes")
install_github("cran/grassr") Install by package version:
library("remotes")
install_version("grassr", "0.8.0") Attach the package and use:
library("grassr")
Maintained by
Austin Semmel
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[Scholar Profile | Author Map]
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First Published: 2026-07-21
Latest Update: 2026-07-21
Description:
Generates a Report Card for rater reliability on binary outcomes from an N x k subject-by-rater rating matrix, on both the inter-rater and intra-rater axes. Each panel coefficient is positioned on a data-generating-process-calibrated reference surface conditioned on the study's rater count, sample size, and prevalence, yielding a pooled percentile (the coefficient's position within the design's achievable agreement range) together with a consistency band on panel quality: the quality levels whose sampling distributions are consistent with the observed value at that design. The panel coefficients are the prevalence-adjusted bias-adjusted kappa (PABAK) of Byrt, Bishop, and Carlin (1993) <doi:10.1016/0895-4356(93)90018-V>, the first-order agreement coefficient (AC1) of Gwet (2008) <doi:10.1348/000711006X126600>, the multi-rater kappa of Fleiss (1971) <doi:10.1037/h0031619>, and the observed intraclass correlation. A cross-coefficient discordance diagnostic (delta-hat) reports the spread of the coefficients' implied panel qualities and flags panels for which no single coefficient is a stable summary by the spread's percentile on a matched null distribution; for such divergent panels the report routes to a pairwise PABAK matrix and per-rater sensitivity and specificity recovered from the latent-class model of Dawid and Skene (1979) <doi:10.2307/2346806>, with the two-rater bounds of Hui and Walter (1980) <doi:10.2307/2530508>.
How to cite:
Austin Semmel (2026). grassr: Context-Conditioned Reporting for Binary Rater Reliability. R package version 0.8.0, https://cran.r-project.org/web/packages/grassr. Accessed 03 Oct. 2026.
Previous versions and publish date:
(2026-09-22 15:30), 0.7.4 (2026-07-21 12:50)
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