Title: Statistical Package for Reliability Data Analysis
Author: Yili Hong, Yimeng Xie, and Zhibing Xu
Description: The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.

Title: Wald's Sequential Probability Ratio Test
Maintainer: Stephane Mikael Bottine <stephane.bottine@gmail.com>
Description: Perform Wald's Sequential Probability Ratio Test on variables with a Normal, Bernoulli, Exponential and Poisson distribution. Plot acceptance and continuation regions, or create your own with the help of closures.

Title: Site Percolation on Square Lattice (SPSL)
Author: Pavel V. Moskalev <moskalefff@gmail.com>
Description: SPSL package provides functionality for labeling of
percolation cluster on 2D & 3D square lattice with various
lattice size, relative fraction of accessible sites (occupation
probability), iso- & anisotropy, von Neumann & Moore

percolation clusters on 2D & 3D square lattice with various

Title: Site Percolation on Square Lattices (SPSL)
Description: Provides basic functionality for labeling
iso- & anisotropic percolation clusters on 2D & 3D square lattices
with various lattice sizes, occupation probabilities, von Neumann &
Moore (1,d)-neighborhoods, and random variables weighting

Title: Selection by Partitioning the Solution Paths
Description: An implementation of the feature Selection procedure by Partitioning the entire Solution Paths
(namely SPSP) to identify the relevant features rather than using a single tuning parameter.
By utilizing the entire solution paths, this procedure can obtain better selection accuracy than
the commonly used approach of selecting only one tuning parameter based on existing criteria,
cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018)
<doi:10.1214/18-EJS1434>). It is more stable and accurate (low false positive and
false negative rates) than other variable selection approaches. In addition, it can be flexibly
coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized
person(given = "Xiaorui (Jeremy)", family = "Zhu", email = "zhuxiaorui1989@gmail.com", role = c("aut", "cre")),
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person(given = "Peng", family = "Wang", email = "wangp9@ucmail.uc.edu", role = c("aut"))