Package: panelr 1.0.1

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.

Authors:Jacob A. Long [aut, cre]

panelr_1.0.1.tar.gz
panelr_1.0.1.zip(r-4.7)panelr_1.0.1.zip(r-4.6)panelr_1.0.1.zip(r-4.5)
panelr_1.0.1.tgz(r-4.6-any)panelr_1.0.1.tgz(r-4.5-any)
panelr_1.0.1.tar.gz(r-4.7-any)panelr_1.0.1.tar.gz(r-4.6-any)
panelr_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
panelr/json (API)

# Install 'panelr' in R:
install.packages('panelr', repos = c('https://jacob-long.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jacob-long/panelr/issues

Pkgdown/docs site:https://panelr.jacob-long.com

Datasets:
  • nlsy - National Longitudinal Survey of Youth data
  • teen_poverty - National Longitudinal Survey of Youth teenage women poverty data
  • WageData - Earnings data from the Panel Study of Income Dynamics

On CRAN:

Conda:

social-sciencestatistics

9.92 score 100 stars 1 packages 234 scripts 4.7k downloads 30 exports 62 dependencies

Last updated from:c26a30339c. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK290
source / vignettesOK545
linux-release-x86_64OK267
macos-release-arm64OK187
macos-oldrel-arm64OK168
windows-develOK235
windows-releaseOK219
windows-oldrelOK195
wasm-releaseOK187

Exports:%<>%%>%are_varyingas_panelas_panel_dataas_pdata.frameasymasym_geebalance_panelcomplete_datafdmfilterget_idget_periodsget_wavehas_gapsheiseis_panelline_plotlong_panelmake_diff_datamake_wb_datamodel_framepanel_datascan_gapsunpanelwbgeewbmwbm_stanwiden_panel

Dependencies:backportsbootbroombroom.mixedclicodacodetoolscpp11crayondigestdplyrfarverforcatsFormulafurrrfuturegenericsggplot2globalsgluegtableisobandjtoolslabelinglatticelifecyclelistenvlme4lmerTestmagrittrMASSMatrixminqanlmenloptrnumDerivpanderparallellypillarpkgconfigpurrrR6rbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangS7sandwichscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrzoo

Introduction to the panelr package
panel_data frames | Regression models | Within-between models | A note on interactions | A note on splines and other basis expansions | Fitting within-between models | Growth curves | Contextual, within, and random effects specifications | Using GEE to fit within-between models | Asymmetric effects | Asymmetric effects for generalized linear models | References
Rendered fromwbm.Rmdusingknitr::rmarkdown

Last update: 2025-12-22
Started: 2019-04-29

Reshaping panel data with long_panel() and widen_panel()
From wide to long | Wave indicators at the end of variable names | Comparing with base R | A more challenging example | Other details | Advanced options | From long to wide
Rendered fromreshape.Rmdusingknitr::rmarkdown

Last update: 2023-02-06
Started: 2019-03-11

Readme and manuals

Help Manual

Help pageTopics
Check if variables are constant or variable over time.are_varying
Estimate asymmetric effects models using first differencesasym
Asymmetric effects models fit with GEEasym_gee
Balance panel data by filling gapsbalance_panel
Filter out entities with too few observationscomplete_data
Estimate first differences models using GLSfdm
Retrieve model formulas from 'wbm' objectsformula.wbm
Retrieve panel_data metadataget_id get_periods get_wave
Check if panel data has gapshas_gaps
Estimate Heise stability and reliability coefficientsheise
Check if object is panel_datais_panel
Plot trends in longitudinal variablesline_plot
Convert wide panels to long formatlong_panel
Generate differenced and asymmetric effects datamake_diff_data
Prepare data for within-between modelingmake_wb_data
Make model frames for panel_data objectsmodel_frame
National Longitudinal Survey of Youth datanlsy
Number of observations used in 'wbm' modelsnobs.wbm
Create panel data framesas_panel as_panel_data as_panel_data.default as_panel_data.pdata.frame as_pdata.frame panel_data
Predictions and simulations from within-between GEE modelspredict.wbgee
Predictions and simulations from within-between modelspredict.wbm simulate.wbm
Print method for WBFormulaprint.WBFormula
Scan for gaps in panel datascan_gaps
Summarize panel data framessummary.panel_data
National Longitudinal Survey of Youth teenage women poverty datateen_poverty
Tidy methods for 'fdm' and 'asym' modelsglance.fdm tidy.asym tidy.fdm
Tidy methods for 'wbgee' modelsglance.wbgee tidy.asym_gee tidy.wbgee
Tidy methods for 'wbm' modelsglance.summ.wbm glance.wbm tidy.summ.wbm tidy.wbm
Convert panel_data to regular data frameunpanel
Earnings data from the Panel Study of Income DynamicsWageData
Panel regression models fit with GEEwbgee
Panel regression models fit via multilevel modelingwbm
Bayesian estimation of within-between modelswbm_stan
Within-Between Model ('wbm') classwbm-class
Convert long panel data to wide formatwiden_panel