Title: Calculate solutions to Kelvin differential equation using Kelvin
Maintainer: Andrew J Barbour <andy.barbour@gmail.com>
Description: Calculate analytic solutions to the Kelvin differential
equation, namely the primary (Bei, Ber) and complementary (Kei,
Collate: 'kelvin-package.R' 'Beir.R' 'Bei.R' 'Ber.R' 'Keir.R' 'Kei.R'

Title: Calculate solutions to Kelvin differential equation using Bessel
Description: Uses Bessel functions to calculate the analytic solutions
to the Kelvin differential equation, namely the primary (Be)
and complementary (Ke) complex functions.
Collate: 'kelvin-package.R' 'zzz.R' 'Beir.R' 'Bei.R' 'Ber.R' 'Keir.R'

Title: Calculate solutions to the Kelvin differential equation using
Description: Uses Bessel functions to calculate the analytic solutions to the
Kelvin differential equation, namely the fundamental (Be) and equivalent
Collate: 'kelvin-package.R' 'Beir.R' 'Keir.R' 'zzz.R'

Title: Calculate Solutions to the Kelvin Differential Equation using
Description: Uses Bessel functions to calculate the
fundamental and complementary analytic solutions to the

Title: Simulate and Visualize Kendall Random Walks and Related
Authors@R: person("Mateusz", "Staniak", email = "mateusz.staniak@math.uni.wroc.pl", role = c("aut", "cre"))
Description: Kendall random walks are a continuous-space Markov chains generated
by the Kendall generalized convolution. This package provides tools
for simulating these random walks and studying distributions
related to them. For more information about Kendall random walks see Jasiulis-Gołdyn (2014) <arXiv:1412.0220>.
Imports: ggplot2, dplyr, tidyr, EnvStats, tibble, nleqslv

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

CB, EG, SG, NEAT CE/PSE and NEC designs, supporting gaussian,
logistic and uniform kernels and unsmoothed and pre-smoothed

Description: 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.
Depends: R(>= 2.11.0), methods, stats, graphics, ltm