Bjorn Andersson, Kenny Branberg and Marie Wiberg
Implements the kernel method of test equating using the
CB, EG, SG, NEAT CE/PSE and NEC designs.

Implements the kernel method of test equating using the
CB, EG, SG, NEAT CE/PSE and NEC designs, supporting gaussian,
logistic and uniform kernels and unsmoothed and pre-smoothed
input data.

Implements the kernel method of test equating using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.
R(>= 2.11.0), methods, stats, graphics, ltm

c(person("Björn", "Andersson", email = "bjoern.h.andersson@gmail.com", role = c("aut", "cre")), person("Kenny", "Bränberg", role = c("aut")), person("Marie", "Wiberg", role = c("aut")))
Implements the kernel method of test equating as defined in von Davier, A. A., Holland, P. W. and Thayer, D. T. (2004) <doi: 10.1007/b97446> and Andersson, B. and Wiberg, M. (2017) <doi: 10.1007/s11336-016-9528-7> using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting Gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.
Björn Andersson [aut, cre],
Kenny Bränberg [aut],
Marie Wiberg [aut]

c(person("Björn", "Andersson", email = "bjoern.h.andersson@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9007-2440")), person("Kenny", "Bränberg", role = c("aut")), person("Marie", "Wiberg", role = c("aut"), comment = c(ORCID = "0000-0001-5549-8262")))
Implements the kernel method of test equating as defined in von Davier, A. A., Holland, P. W. and Thayer, D. T. (2004) <doi:10.1007/b97446> and Andersson, B. and Wiberg, M. (2017) <doi:10.1007/s11336-016-9528-7> using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting Gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.
Björn Andersson [aut, cre] (<https://orcid.org/0000-0002-9007-2440>),
Kenny Bränberg [aut],
Marie Wiberg [aut] (<https://orcid.org/0000-0001-5549-8262>)

New kernel-based test and fast tests for detecting change-points or changed-intervals where the distributions abruptly change. They work well particularly for high-dimensional data.
Song, H. and Chen, H. (2022)
<arXiv:2206.01853>.