c(person("Hongyu", "Mou", , "hongyumou@g.ucla.edu", role = c("aut")),
person("Licheng", "Liu", ,"liulch@mit.edu", role = c("aut")),
person("Yiqing", "Xu", ,"yiqingxu@stanford.edu", role = c("aut", "cre"),
Description: Visualizes panel data. It has three main functionalities: (1) it visualizes treatment status and missing values in a panel dataset; (2) it plots an outcome variable (or any variable) in a time-series fashion; (3) it visualizes bivariate relationships of two variables by unit or in aggregate.
Imports: ggplot2 (>= 2.1.0), gridExtra, grid, dplyr

Description: Visualizes panel data. It has two main functionalities: (1) it visualizes the treatment and missing-value statuses of each observation in a panel/time-series-cross-sectional (TSCS) dataset; and (2) it plots the outcome variable (either continuous or discrete) in a time-series fashion.

c(person("Hongyu", "Mou", , "hongyumou5@gmail.com", role = c("aut")),

Description: Leading/lagging a panel, creating dummy variables,
taking panel differences, looking for panel autocorrelations,
and more. Implemented via a 'data.table' back end.

Title: Aggregate Longitudinal Survey Data
Description: Aggregate Business Tendency Survey Data (and other qualitative
surveys) to time series at various aggregation levels. Run aggregation of
survey data in a speedy, re-traceable and a easily deployable way.
Aggregation is substantially accelerated by use of data.table.
This package intends to provide an interface that is less general and abstract than data.table but rather geared towards

Panelaggregation aims at providing an interface that is more geared towards
Author: Matthias Bannert [aut, cre], Gabriel Bucur [aut]

Package: panelr
Title: Regression Models and Utilities for Repeated Measures and Panel
Data
Version: 0.7.0
Authors@R: person("Jacob A.", "Long", email = "long.1377@osu.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1582-6214"))
Description: Provides an object type and associated tools for storing and
wrangling panel data. Implements several methods for creating regression
models that take advantage of the unique aspects of
panel data. Among other capabilities, automates the "within-between"
(also known as "between-within" and "hybrid") panel regression specification
that combines the desirable aspects of both fixed effects and random effects
econometric models and fits them as multilevel models
(Allison, 2009 <doi:10.4135/9781412993869.d33>;
Bell & Jones, 2015 <doi:10.1017/psrm.2014.7>). These models can also be
estimated via generalized estimating equations
(GEE; McNeish, 2019 <doi:10.1080/00273171.2019.1602504>) and Bayesian
estimation is (optionally) supported via 'Stan'.
Supports estimation of asymmetric effects models via first differences
(Allison, 2019 <doi:10.1177/2378023119826441>) as well as a generalized
linear model extension thereof using GEE.
URL: https://panelr.jacob-long.com
BugReports: https://github.com/jacob-long/panelr
Depends: R (>= 3.4.0), lme4
Imports: crayon, dplyr, Formula, ggplot2, jtools (>= 2.0.1), lmerTest,
magrittr, methods, purrr, rlang (>= 0.3.0), stringr, tibble
Suggests: brms, broom, car, clubSandwich, geepack, nlme, plm, sandwich,
skimr, tidyr (>= 0.8.3), testthat, covr, knitr, rmarkdown
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1.9000
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-05-13 14:41:45 UTC; jlong
Author: Jacob A. Long [aut, cre] (<https://orcid.org/0000-0002-1582-6214>)
Maintainer: Jacob A. Long <long.1377@osu.edu>
Repository: CRAN
Date/Publication: 2019-05-16 11:10:07 UTC