stats, grDevices, utils, matrixStats (>= 0.8.1), Hmisc,
impute, splines, foreach, doParallel, preprocessCore, survival,
parallel, GO.db, AnnotationDbi, Rcpp (>= 0.11.0)

Functions for the analysis of whole-genome sequencing studies to simultaneously detect the existence, and estimate the locations of association signals at genome-wide scale. The functions allow genome-wide association scan, candidate region scan and single window test.

R Client for the World Health Organization API
Provides programmatic access to the World Health Organization API.

Reads in WIN pickfile and waveform file, prepares data for RSEIS

Read WIN (Japanese Seismic data)  pickfile and waveform files and prepare data for RSEIS

Reads in WIN pickfile and waveform file, prepares data for
RSEIS

c(person(given = "Ying",
family = "Cui",
role = c("aut", "cre"),
email = "cuiyingbeicheng@gmail.com"),
person(given = "Bo",
family = "Huang",
role = "aut"))
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.

Variable Selection for Highly Correlated Predictors
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).

Statistical Inference in Weighted Lindley Distribution
Yu-Hyeong Jang, SungBum Kim, Hyun-Ju Jung and Hyoung-Moon Kim
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.
Wang, M., Wang, W. (2017) <doi:10.1080/03610918.2014.970696>.
LindleyR, pracma, boot, nleqslv, stats, goftest, cubature,
bbmle