Package index
Model fitting
The main estimator for panel GLMs (pooled, within, between and random effects) and the two models built on it for costs and structural zeros.
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panglm() - Fit a generalized linear model for panel data
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twopart() - Two-part model for panel cost data
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panhurdle() - Fixed-effects hurdle model for panel count data
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panglm-package - panglm: Generalized Linear Models for Panel Data
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gaussianfamily()poissonfamily()binomialfamily()negbinfamily()gammafamily()orderedfamily()betafamily()tobitfamily() - Family specifications for panglm
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did() - Two-way fixed-effects difference-in-differences
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eventstudy() - Event-study regression with a pre-trend test
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attgt() - Group-time average treatment effects for staggered adoption
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etwfe() - Extended two-way fixed-effects difference-in-differences
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trendplot() - Plot raw outcome trends of a difference-in-differences design
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treatmentplot() - Plot the treatment timing of a staggered design
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panelplot() - Plot a panel as a unit by period heat map
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eventplot() - Compare event studies on one plot
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paniv() - Instrumental-variable estimators for linear panel models
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pangmm() - Difference and system GMM for dynamic linear panel models
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panselect() - Sample-selection correction for linear panel models
Heterogeneity
Quantile regression and latent classes. Random slopes are fitted by panglm() with model = "random" and its random argument.
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panqr() - Quantile regression for panel data with individual effects
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panfmm() - Latent class (finite mixture) models for panel data
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panfmmselect() - Compare latent class panel models with different numbers of classes
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panhausman() - Hausman specification test (fixed vs. random effects)
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pandispersiontest() - Pearson chi-squared/df overdispersion test
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jointtest() - Joint Wald test of coefficients
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lineartest() - Test whether the effect of a regressor is linear
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summary(<panglm>) - Summarize a panglm fit
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vcov(<panglm>) - Recompute the coefficient covariance matrix for a panglm fit
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confint(<panglm>) - Confidence intervals for panglm coefficients
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predict(<panglm>) - Predict from a fitted panglm model
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predict(<panordered>) - Predict from an ordered panel model
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fitted(<panglm>) - Extract fitted values from a panglm model
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logLik(<panglm>) - Extract the model log-likelihood
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model.matrix(<panglm>) - Design matrix of a panglm fit
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residuals(<panglm>) - Extract residuals from a panglm model
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fixef()ranef() - Individual effects of a panel model
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plot(<panglm>) - Plot a fitted panglm model
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coefplot() - Compare coefficient estimates of several fitted models
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partialeffects() - Average partial effects
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plot(<panglm_ape>) - Plot average partial effects
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effectcurve() - Estimated effect curve of a regressor
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plot(<panglm_effectcurve>) - Plot an effect curve
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predict(<twopart>) - Predict from a two-part model
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confint(<twopart>) - Confidence intervals for a two-part model
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plot(<twopart>) - Plot a two-part model
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coef(<panglm_hurdle>)vcov(<panglm_hurdle>)nobs(<panglm_hurdle>)confint(<panglm_hurdle>)summary(<panglm_hurdle>)print(<summary.panglm_hurdle>) - Coefficient methods for the hurdle model
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plot(<panglm_hurdle>) - Plot a fitted panglm hurdle model
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vcov(<panglm_eventstudy>)nobs(<panglm_eventstudy>)confint(<panglm_eventstudy>)summary(<panglm_eventstudy>)print(<summary.panglm_eventstudy>)logLik(<panglm_eventstudy>) - Methods for event-study fits
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plot(<panglm_eventstudy>) - Plot event-study coefficients
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summary(<panglm_attgt>) - Aggregate group-time average treatment effects
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plot(<panglm_attgt>)plot(<summary.panglm_attgt>) - Plot an event study from group-time effects
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summary(<panglm_etwfe>) - Average treatment effects on the treated from an ETWFE fit
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plot(<panglm_etwfe>)plot(<summary.panglm_etwfe>) - Plot ETWFE treatment effects
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summary(<paniv>) - Summarize a paniv fit
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vcov(<paniv>) - Covariance of a paniv fit
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predict(<paniv>) - Predictions from a panel instrumental-variables fit
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summary(<pangmm>) - Summarize a pangmm fit
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vcov(<pangmm>) - Covariance of a pangmm fit
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predict(<pangmm>) - Predictions from a dynamic panel GMM fit
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plot(<pangmm>)plot(<paniv>)plot(<panselect>) - Plot the coefficients of a GMM, IV or selection model
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summary(<panselect>) - Summarize a panselect fit
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vcov(<panselect>) - Covariance of a panselect fit
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predict(<panselect>) - Predictions from a panel sample-selection fit
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summary(<panqr>) - Summarize a panqr fit
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vcov(<panqr>) - Covariance of a panqr fit
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plot(<panqr>) - Plot a panel quantile regression fit
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summary(<panfmm>) - Summarize a panfmm fit
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vcov(<panfmm>) - Covariance of a panfmm fit
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predict(<panfmm>) - Predictions from a latent class panel model
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plot(<panfmm>) - Plot a latent class (finite mixture) model
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tidy(<panglm>) - Tidy a panglm fit into a data frame of coefficients
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glance(<panglm>) - One-row model summary of a panglm fit
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tidy(<panglm_hurdle>)glance(<panglm_hurdle>)tidy(<twopart>)glance(<twopart>)tidy(<pangmm>)glance(<pangmm>)tidy(<paniv>)glance(<paniv>)tidy(<panqr>)glance(<panqr>)tidy(<panselect>)glance(<panselect>)tidy(<panfmm>)glance(<panfmm>)tidy(<panglm_attgt>)glance(<panglm_attgt>)tidy(<panglm_etwfe>)glance(<panglm_etwfe>)tidy(<panglm_eventstudy>)glance(<panglm_eventstudy>)tidy(<panglm_hausman>)glance(<panglm_hausman>)tidy(<panglm_jointtest>)glance(<panglm_jointtest>)tidy(<panglm_dispersiontest>)glance(<panglm_dispersiontest>) - Tidy and glance methods for the panglm model classes
Data
Health economics panels for teaching: one simulated panel with known truth and five real public panels.
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copd - Synthetic COPD follow-up panel with costs and a staggered programme
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docvisits - German health-care demand panel (doctor and hospital visits, GSOEP)
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meps - Health care expenditure and use of US adults, MEPS panel 23 (2018-2019)
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medicaid - Health insurance coverage and the ACA Medicaid expansion, US states 2009-2019
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healthspend - Health expenditure, income and population health: a country-year panel
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cigar - Cigarette demand in US states, 1963-1992 (Baltagi and Levin)