Title: Data Estimation using Weighted Averages of Multiple Regressions
Author: Olivia Cheronet, John A. Finarelli
Description: For multivariate datasets, this function enables the estimation of missing data using the Weighted AVERage of all possible Regressions using the data available.
Packaged: 2016-02-12 10:14:25 UTC; cheronetolivia

Title: Semiparametric Proportional Means Regression of Weighted
Description: Implements inferential and graphic procedures for the semiparametric proportional
means regression of weighted composite endpoint of recurrent event and death (Mao and Lin,

Title: Weighted Mixed-Effects Models, using Multilevel Pseudo Maximum
Author: Paul Bailey, Claire Kelley, Trang Nguyen, Huade Huo.
Imports: numDeriv, minqa, statmod, Rmpfr, MASS, NPflow, Rcpp
Suggests: testthat, knitr, rmarkdown, EdSurvey
Description: Run mixed-effects models that include weights at every level. The 'WeMix' package fits a Weighted Mixed model, also known as a multilevel, mixed, or hierarchical linear models. The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in 'R', 'WeMix' is unique in implementing methods for mixed models using weights at multiple levels. The model is fit using adaptive quadrature following the methodology of Rabe-Hesketh, S., and Skrondal, A. (2006) <doi:10.1111/j.1467-985X.2006.00426.x>.
imply endorsement by the U.S. government.

Imports: numDeriv, statmod, Rmpfr, NPflow, Rcpp
Description: Run mixed-effects models that include weights at every level. The WeMix package fits a Weighted Mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. The model is fit using adaptive quadrature.