Title: Assessing the Partial Association Between Ordinal Variables
Description: An implementation of the unified framework for assessing partial association
between ordinal variables after adjusting for a set of covariates (Dungang Liu, Shaobo
Li, Yan Yu and Irini Moustaki (2020), accepted by the Journal of the American
Statistical Association). This package provides a set of tools to quantify, visualize,
and test partial associations between multiple ordinal variables. It can produce a number
of $phi$ measures, partial regression plots, 3-D plots, and $p$-values for testing
c(person(given = "Xiaorui (Jeremy)", family = "Zhu",
person(given = "Dungang", family = "Liu",
person(given = "Yuejie ", family = "Chen",
Maintainer: Xiaorui (Jeremy) Zhu <zhuxiaorui1989@gmail.com>
Imports: VGAM, copBasic, pcaPP (>= 1.9-73), methods, foreach (>=
3.5.3), progress (>= 1.2.0), plotly, copula
Suggests: doParallel (>= 1.0.11), tidyverse, goftest, faraway, ordinal,
rms, testthat, mgcv, PResiduals, knitr, rmarkdown, truncdist
Author: Xiaorui (Jeremy) Zhu [aut, cre],

Description: An implementation of the unified framework for assessing partial
association between ordinal variables after adjusting a set of covariates
(Dungang Liu, Shaobo Li, Yan Yu and Irini Moustaki (2020). Accepted by JASA).
This package provides a set of tools to quantify partial association, conduct
hypothesis testing for partial association, visualize partial regression models,
and diagnose the specifications of each fitted model. This framework is
based on the surrogate approach described in Liu and Zhang (2017) (<doi:10.1080/01621459.2017.1292915>).
Imports: VGAM, copBasic, pcaPP (>= 1.9-73), methods, doParallel (>=
GGally, goftest, gridExtra, utils (>= 3.5.3), progress (>=
Suggests: faraway, ordinal, rms, testthat, mgcv, PResiduals, knitr,

Title: Streamline Physical Activity Research
c("Paul", "R."), "Hibbing", email = "paulhibbing@gmail.com",
"Centers for Disease Control and Prevention", role = "ctb"))
Description: A collection of utilities that are useful for a broad range of
tasks that are common in physical activity research, including the
following: creation of Bland-Altman plots, formatted descriptive
statistics, metabolic calculations (e.g. basal metabolic rate predictions)
and conversions, demographic calculations (age and age-for-body-mass-index
percentile), bout analysis of moderate-to-vigorous intensity physical
activity, and analysis of bout detection algorithm performance.
Imports: AGread (>= 0.1.2), dplyr (>= 0.7), ggplot2 (>= 2.2), graphics,
lazyeval (>= 0.2), magrittr (>= 1.5), matchingMarkets (>=
Centers for Disease Control and Prevention [ctb]