Packaged: 2012-03-28 11:23:08 UTC; greatsage

Packaged: 2012-04-30 15:34:32 UTC; greatsage

Packaged: 2012-06-08 16:25:52 UTC; greatsage

Packaged: 2012-06-26 10:55:50 UTC; greatsage

Packaged: 2012-06-29 12:57:02 UTC; greatsage

Packaged: 2012-08-28 16:03:12 UTC; greatsage

Packaged: 2012-08-23 14:25:24 UTC; greatsage

Packaged: 2013-03-20 16:56:31 UTC; greatsage

Packaged: 2014-01-08 11:55:25 UTC; greatsage

Title: A Package to Test for Pleiotropic Effects
Packaged: 2015-07-06 07:45:43 UTC; greatsage

Packaged: 2017-05-08 18:48:09 UTC; greatsage

Title: A Package to Test for Multi-Trait Association

Title: Multivariate Pseudo-Random Number Generation
Description: Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart.
Packaged: 2017-06-03 19:39:30 UTC; rawanallozi

Packaged: 2018-01-10 20:03:41 UTC; rawanallozi

Description: Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart. The details of the method are explained in Demirtas (2004) <DOI:10.22237/jmasm/1099268340>.

Title: Bias, Precision, and Power for Multi-Level Random Regressions
Maintainer: Yimen G. Araya-Ajoy <yimencr@gmail.com>
Description: Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.

Title: Multiply Robust Methods for Missing Data Problems
Description: Multiply robust estimation for population mean (Han and Wang 2013) <doi:10.1093/biomet/ass087>, regression analysis (Han 2014) <doi:10.1080/01621459.2014.880058> (Han 2016) <doi:10.1111/sjos.12177> and quantile regression (Han et al. 2019) <doi:10.1111/rssb.12309>.
Packaged: 2019-02-27 19:38:29 UTC; Administrator