Authors@R: c( person(given = "Jonathan", family = "Gilligan",
Suggests: broom (>= 0.5), knitr (>= 1.22), purrr (>= 0.3), rmarkdown

Suggests: broom (>= 0.5), knitr (>= 1.22), rmarkdown (>= 1.12),

Title: Tools for Tall Distributed Matrices
Description: Many data science problems reduce to operations on very tall,
skinny matrices.  However, sometimes these matrices can be so tall that they
are difficult to work with, or do not even fit into main memory.  One
strategy to deal with such objects is to distribute their rows across
several processors.  To this end, we offer an 'S4' class for tall, skinny,
distributed matrices, called the 'shaq'.  We also provide many useful
numerical methods and statistics operations for operating on these
distributed objects.  The naming is a bit "tongue-in-cheek", with the class
a play on the fact that 'Shaquille' 'ONeal' ('Shaq') is very tall, and he
person("Drew", "Schmidt", role = c("aut", "cre"), email="wrathematics@gmail.com"),
person("Wei-Chen", "Chen", role = "aut"),
person("Mike", "Matheson", role = "aut"),
person("George", "Ostrouchov", role = "aut"),
MailingList: Please send questions and comments regarding pbdR to

Author: Dave Zes, Jimmy Lewis, Dana Landis @ Korn/Ferry International
Description: Create, Simulate, Fit, Solve k-Cube Thurstonian IRT Models

Depends: R (>= 3.0.0), mvtnorm, snowfall, corpcor

person("Marcel", "Kremer", role = c("aut", "cre"),
Description: Provides the density and distribution function of the bivariate
K-copula by Wollschläger and Schäfer (2016) <doi:10.21314/JOR.2016.342>.
Author: Marcel Kremer [aut, cre] (<https://orcid.org/0000-0001-9130-7670>)

Title: Kernel Change Point Detection on the Running Statistics
Description: The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) <arXiv:1202.3878> is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, <doi:10.1016/j.ins.2018.03.010>). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0.
Depends: RColorBrewer, stats, utils, graphics, roll, foreach,