Title: Sequential Normal Scores in Statistical Process Management
Authors@R: c(person("Victor", "Tercero", email = "victor.tercero@tec.mx", role=c("aut")),
person("Luis", "Benavides", email = "luisbv@tec.mx", role = c("aut", "cre")),
person("Jorge", "Merlo", email = "jorgemerlo24@gmail.com", role = c("ctb")))
Description: The methods discussed in this package are new non-parametric methods
based on sequential normal scores 'SNS' (Conover et al (2017)
<doi:10.1080/07474946.2017.1360091>), designed for sequences of observations,
usually time series data, which may occur singly or in batches,
and may be univariate or multivariate. These methods are designed
to detect changes in the process, which may occur as changes in location
(mean or median), changes in scale (standard deviation, or variance), or
other changes of interest in the distribution of the observations,
over the time observed. They usually apply to large data sets,
so computations need to be simple enough to be done in a reasonable
time on a computer, and easily updated as each new observation
(or batch of observations) becomes available. Some examples and more detail
in 'SNS' is presented in the work by Conover et al (2019) <arXiv:1901.04443>.

Title: Standard and Nonstandard Statistical Models and Methods for Test
Maintainer: Jorge Gonzalez Burgos <jgonzale@mat.puc.cl>
Description: The package contains functions to perform various models
and methods for test equating. It currently implements the
Kernel Method of Equating using the (traditional) Gaussian
kernel and also nonstandard alternative kernels such as the
logistic and uniform. More kernels will be implemented in the
near future as well as other nonstandard methods of equating.

Description: The package contains functions to perform various models and
methods for test equating. It currently implements the traditional
mean, linear and equipercentile equating methods, as well as the
mean-mean, mean-sigma, Haebara and Stocking-Lord IRT linking methods.
It also supports newest methods such that local equating, kernel
equating (using Gaussian, logistic and uniform kernels), and IRT
parameter linking methods based on asymmetric item characteristic
functions. Functions to obtain both standard error of equating (SEE)
and standard error of equating difference between two equating
functions (SEED) are also implemented for the kernel method of