Description: Read WIN (Japanese Seismic data)  pickfile and waveform files and prepare data for RSEIS
Packaged: Mon Apr 14 08:12:37 2008; lees

Packaged: Wed Apr 16 06:30:45 2008; lees

Packaged: Fri Apr 25 13:42:38 2008; lees

Packaged: Mon Jan 26 11:23:52 2009; lees

Description: Reads in WIN pickfile and waveform file, prepares data for

Packaged: Mon Mar 31 17:01:51 2008; lees

Description: Calculate the win statistics (win ratio, net benefit and win odds) for prioritized multiple endpoints, plot the win statistics and win proportions over study time if at least one time-to-event endpoint is analyzed, and simulate datasets with dependent endpoints. The package can handle any type of outcomes (continuous, ordinal, binary, time-to-event) and allow users to perform stratified analysis and inverse probability of censoring weighting (IPCW) analysis.

Title: Variable Selection for Highly Correlated Predictors
Description: It proposes a novel variable selection approach taking into account the correlations that may exist between the predictors of the design matrix in a high-dimensional linear model. Our approach consists in rewriting the initial high-dimensional linear model to remove the correlation between the predictors and in applying the generalized Lasso criterion. For further details we refer the reader to the paper <arXiv:2007.10768> (Zhu et al., 2020).
Imports: Matrix, genlasso, tibble, MASS, ggplot2

Title: Statistical Inference in Weighted Lindley Distribution
Author: Yu-Hyeong Jang, SungBum Kim, Hyun-Ju Jung and Hyoung-Moon Kim
Description: Estimate the parameters of weighted Lindley distribution, and provide some
useful results like goodness of fit or confidence interval etc.
Ghitany, M., Alqallaf, F., Al-Mutairi, D., Husain, H. (2011) <doi:10.1016/j.matcom.2010.11.005>.
Hyoung-Moon Kim. and Yu-Hyeong Jang. (2020) submitted.
Imports: LindleyR, pracma, boot, nleqslv, stats, goftest, cubature,

Author: Yu-Hyeong Jang, Sungbum Kim, Hyun-Ju Jung and Hyoung-Moon Kim

Title: Whitening Logistic Regression for Variable Selection
Description: It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion. For further details we refer the reader to the paper Zhu et al. (2022) <arXiv:2206.14850>.
Imports: cvCovEst, genlasso, tibble, MASS, ggplot2, Matrix, glmnet,