Suggests: knitr, MplusAutomation (>= 0.7), rmarkdown
Imports: lavaan (>= 0.5-22), ggplot2, tidyr, stringr

Authors@R: c(person("Anthony", "Raborn", email = "anthony.w.raborn@gmail.com", role = c("aut", "cre")), person("Walter", "Leite", email = "Walter.Leite@coe.ufl.edu", role = "aut"))

Title: Shrinkage Covariance Matrix Estimators
Description: This package provides nonparametric Stein-type shrinkage estimators of the covariance matrix that are suitable in high-dimensional settings, that is when the number of variables is larger than the sample size.

Description: This package provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.

Description: The package ShrinkCovMat provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.

Description: Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.

Authors@R: person("Anestis", "Touloumis", role = c("aut", "cre"),
Author: Anestis Touloumis [aut, cre] (0000-0002-5965-1639)

Title: Several Shrinkage Effect-Size Estimators
Author: Corey M. Yanofsky, Zahra Montazeri, Marta Padilla, Alaa Ali and David R. Bickel
Description: Several shrinkage estimators of the effect-size of a parameter of interest.
Depends: R (>= 2.14.0), methods, PsiHat, multtest, limma
Packaged: 2015-10-23 15:11:42 UTC; martapadilla

Title: Signal Extraction Approach for Sparse Multivariate Response
Description: Methods for regression with high-dimensional predictors and  univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.