Title: Measuring Agreement Using Krippendorff's Alpha Coefficient
Description: Provides tools for applying Krippendorff's Alpha methodology <DOI:10.1080/19312450709336664>. The framework supports common and user-defined distance functions, and can accommodate any number of units, any number of coders, and missingness. Bootstrap inference is permitted, and the computation can be done in parallel.
Packaged: 2020-06-01 16:10:19 UTC; jphughesjr

Suggests: parallel, pbapply, testthat (>= 3.0.0)
Packaged: 2021-11-03 13:17:50 UTC; johnhughes

Packaged: 2022-05-11 18:19:53 UTC; jphughesjr

Packaged: 2021-01-14 18:00:28 UTC; jphughesjr

Suggests: parallel, pbapply, spam, testthat (>= 3.0.0)
Description: Provides tools for applying Krippendorff's Alpha methodology <DOI:10.1080/19312450709336664>. Both the customary methodology and Hughes' methodology <DOI:10.48550/arXiv.2210.13265> are supported, the former being preferred for larger datasets, the latter for smaller datasets. The framework supports common and user-defined distance functions, and can accommodate any number of units, any number of coders, and missingness. Interval estimation can be done in parallel for either methodology.
Packaged: 2022-10-25 15:50:26 UTC; johnhughes

Author: Youyi Fong <youyifong@gmail.com>, Saheli Datta, Krisztian Sebestyen
Description: Testing methods for kernel-based regression models.

Description: Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence.

Authors@R: c(person("Youyi", "Fong", role = "cre", email = "youyifong@gmail.com"), person("Saheli", "Datta", role = "aut", email = "saheli8@gmail.com"), person("Krisztian", "Sebestyen", role = "aut", email = "ksebestyen@gmail.com"), person("Steve", "Park", role = "ctb"), person("Dave", "Geyer", role = "ctb"))

Description: Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence. This package is described in Fong et al. (2015) <DOI:10.1093/biostatistics/kxu056>.