c(
person("Jocelyn T.", "Chi", role=c("aut", "cre"), email="jtchi@ncsu.edu"),
person("Eric C.", "Chi", role=c("aut", "ctb"), email="ecchi1105@gmail.com"),
person("Richard G.", "Baraniuk", role="aut", email="richb@rice.edu"))
Software for k-means clustering of partially
observed data from Chi, Chi, and Baraniuk (2016) <doi:10.1080/00031305.2015.1086685>.
Jocelyn T. Chi [aut, cre],
Eric C. Chi [aut, ctb],
Richard G. Baraniuk [aut]

Provides basic functions for Continuation-Passing Style development.

Geospatial Kriging with Metropolis Sampling
Jason S. Byers [aut, cre], Jeff Gill [aut], James E. Monogan III [aut]
Estimates kriging models for geographical point-referenced data. Method is described in Monogan and Gill (2016) <doi:10.1017/psrm.2015.5>.

Jason S. Byers [aut, cre], Jeff Gill [aut], James E. Monogan III [aut], Jamie Carson [aut], Le Bao [aut]

Jason S. Byers [aut, cre], Le Bao [aut], James E. Monogan III [aut], Jamie Carson [aut], Jeff Gill [aut]

Jason S. Byers [aut, cre], Le Bao [aut], Jamie Carson [aut], Jeff Gill [aut]
Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) <doi:10.1177/1532440020930197>.

Simple and highly optimized ordinary kriging algorithm to plot geographical data

An implementation of a simple and highly optimized ordinary kriging algorithm to plot geographical data.

Measuring Agreement Using Krippendorff's Alpha Coefficient
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.

parallel, pbapply, spam, testthat (>= 3.0.0)
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.