Title: Analysis of Accelerated Destructive Degradation Test Data
Author: Yili Hong, Yimeng Xie, Zhongnan Jin, and Caleb King
Description: Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the collected data, a thermal index (TI) is estimated. The TI can be useful for material rating and comparison. This package implements the traditional method based on the least-squares method, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline methods, and the corresponding methods for estimating TI for polymeric materials. The traditional approach is a two-step approach that is currently used in industrial standards, while the parametric method is widely used in the statistical literature. The semiparametric method is newly developed. Both the parametric and semiparametric approaches allow one to do statistical inference such as quantifying uncertainties in estimation, hypothesis testing, and predictions. Publicly available datasets are provided illustrations. More details can be found in Jin et al. (2017).

Authors@R: c(person("Luis", "Cayuela", role = "aut",
person("Marcelino", "De la Cruz", role = c("aut", "cre"),
Description: Data sets used in Cayuela and De la Cruz (2022, ISBN:978-84-8476-705-3).
Packaged: 2022-02-17 20:56:21 UTC; marcelino
Maintainer: Marcelino De la Cruz <marcelino.delacruz@urjc.es>

Maintainer: Carlos J. Gil Bellosta <cgb@datanalyics.com>
Description: Anderson-Darling GoF test with p-value calculation based
on Marsaglia's 2004 paper "Evaluating the Anderson-Darling

Maintainer: Carlos J. Gil Bellosta <cgb@datanalytics.com>
Description: Anderson-Darling GoF test with p-value calculation based on Marsaglia's 2004 paper "Evaluating the Anderson-Darling Distribution"

Title: An Interpretation of the ADM method - automated detection
Description: Robust change point detection using ADM3 algorithm.

Title: Algorithms using Alternating Direction Method of Multipliers
Authors@R: person("Kisung", "You", email = "kyou@nd.edu", role = c("aut", "cre"))
Description: Provides algorithms to solve popular optimization problems in statistics such as regression or denoising based on Alternating Direction Method of Multipliers (ADMM).
See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.