for Hierarchical Models," by Perry (2015).

Authors@R: c(person(c("Patrick", "O."), "Perry", role = c("aut", "cre"),
person("Timothy", "Sweetser", role = "ctb",
Description: Implements methods from the paper
"Fast Moment-Based Estimation for Hierarchical Models," by Perry (2016).
Imports: lme4, bigmemory, foreach, logging

person("Timothy", "Sweetser", role = "ctb"),
person("Kyle", "Schmaus", role = "ctb"),
person("Ningshan", "Zhang", role = c("aut", "ctb")))
Description: Fast moment-based hierarchical model fitting. Implements
"Fast Moment-Based Estimation for Hierarchical Models," by Perry (2017)
"Fitting a Deeply Nested Hierarchical Model to a Large Book Review
Dataset Using a Moment-Based Estimator," by Zhang, Schmaus, and Perry
Imports: abind, bigmemory, foreach, lme4, logging

Description: Measure based exploratory data analysis. Some of the functions call interactive apps programmed with the package shiny to provide flexible selection options.
Imports: mgcv, extracat, scagnostics, diptest, hexbin,scales, dplyr,
magrittr, shiny, seriation, ggplot2, gridExtra, GGally, Rcpp,

Description: Measure based exploratory data analysis. Some of the functions call interactive apps programmed with the package 'shiny' to provide flexible selection options. Version 1.0.1 uses dcor() from 'energy' instead of wdcor() from 'extracat'.
Imports: scagnostics, mgcv, energy, diptest, hexbin,scales, dplyr,

Description: Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. A major goal of 'mbir' is to programmatically detect appropriate statistical tests to run in lieu of relying on practitioners to determine correct stepwise procedures independently.
Copyright: Segments of the package are based upon Will G. Hopkins'
work. See vignette and COPYRIGHT file for details.

person("Aaron", "Caldwell", role = "aut"))

Imports: graphics, stats, utils, effsize, psych
Packaged: 2018-09-17 00:16:18 UTC; ath_sportsci

Packaged: 2019-01-22 17:40:10 UTC; ath_sportsci

Description: This package provides linear models based
on Theil-Sen single median and Siegel repeated
medians. They are very robust (29 or 50 percent breakdown
point, respectively), and if no outliers are present,
Packaged: Thu Aug 11 20:34:46 2005; Lukke