panelr: Regression Models and Utilities for Repeated Measures and Panel Data

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.

Version: 0.7.1
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
Published: 2019-07-12
Author: Jacob A. Long ORCID iD [aut, cre]
Maintainer: Jacob A. Long <long.1377 at osu.edu>
BugReports: https://github.com/jacob-long/panelr
License: MIT + file LICENSE
URL: https://panelr.jacob-long.com
NeedsCompilation: no
Citation: panelr citation info
Materials: README NEWS
In views: Econometrics
CRAN checks: panelr results

Downloads:

Reference manual: panelr.pdf
Vignettes: Reshaping panel data with 'long_panel()' and 'widen_panel()'
Introduction to the 'panelr' package
Package source: panelr_0.7.1.tar.gz
Windows binaries: r-devel: panelr_0.7.0.zip, r-release: panelr_0.7.0.zip, r-oldrel: panelr_0.7.1.zip
OS X binaries: r-release: panelr_0.7.1.tgz, r-oldrel: panelr_0.7.1.tgz
Old sources: panelr archive

Reverse dependencies:

Reverse suggests: insight

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