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. (2021) <doi:10.1007/s42952-020-00097-y>.
Wang, M., Wang, W. (2017) <doi:10.1080/03610918.2014.970696>.

Whitening Logistic Regression for Variable Selection
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>.
cvCovEst, genlasso, tibble, MASS, ggplot2, Matrix, glmnet,
corpcor

Regression Analysis Based on Win Loss Endpoints
Use various regression models for the analysis of win loss endpoints
adjusting for non-binary and multivariate covariates.

William and Mary Parser For Bruker-Ultraflex Mass Spec Data
William Cooke, Maureen Tracy and Dariya Malyarenko
The package parses Bruker Ultraflex TOF mass spectrometry
data and experimental parameter files into R structures for
further signal processing and statistical analysis.

GUI implementing Bayesian working memory models
A GUI R implementation of hierarchical Bayesian models of
working memory, used for analyzing change detection data.

gtools, gWidgets, gWidgetsRGtk2, coda, cairoDevice

R (>= 2.10), gtools, gWidgets, gWidgetsRGtk2, coda,
cairoDevice

A GUI R implementation of hierarchical Bayesian models of working memory, used for analyzing change detection data.

GUI Implementing Bayesian Working Memory Models

Discriminant Analysis Methods by Weight Mahalanobis Distance and
bayes
Distance discriminant analysis method is one of
classification methods according to multiindex performance
parameters.However,the traditional Mahalanobis distance
discriminant method treats with the importance of all
parameters equally,and exaggerates the role of parameters which
changes a little.The weighted Mahalanobis distance is used in
discriminant analysis method to distinguish the importance of
each parameter.In the concrete application,firstly based on the
principal component analysis scheme,a new group of parameters
and their corresponding percent contributions of the parameters
are calculated ,and the weighted matrix is regarded as the
diagonal matrix of the contributions rates.Setting data to
standardization,then the weighted Mahalanobis distance can be
calculated.Besides the methods metioned above,bayes method is
also given.