Regression models

wbm()

Panel regression models fit via multilevel modeling

wbgee()

Panel regression models fit with GEE

fdm()

Estimate first differences models using GLS

asym()

Estimate asymmetric effects models using first differences

asym_gee()

Asymmetric effects models fit with GEE

wbm_stan()

Bayesian estimation of within-between models

Panel data wrangling

panel_data() as_pdata.frame() as_panel_data() as_panel()

Create panel data frames

widen_panel()

Convert long panel data to wide format

long_panel()

Convert wide panels to long format

summary(<panel_data>)

Summarize panel data frames

complete_data()

Filter out entities with too few observations

model_frame()

Make model frames for panel_data objects

unpanel()

Convert panel_data to regular data frame

is_panel()

Check if object is panel_data

Model utilities

tidy.wbm() glance.wbm()

Tidy methods for wbm models

tidy.asym_gee() tidy.wbgee() glance.wbgee()

Tidy methods for wbgee models

tidy.fdm() glance.fdm()

Tidy methods for fdm and asym models

predict(<wbm>) simulate(<wbm>)

Predictions and simulations from within-between models

predict(<wbgee>)

Predictions and simulations from within-between GEE models

formula(<wbm>)

Retrieve model formulas from wbm objects

nobs(<wbm>)

Number of observations used in wbm models

Other utilities

are_varying()

Check if variables are constant or variable over time.

make_wb_data()

Prepare data for within-between modeling

make_diff_data()

Generate differenced and asymmetric effects data

get_wave() get_id() get_periods()

Retrieve panel_data metadata

line_plot()

Plot trends in longitudinal variables

heise()

Estimate Heise stability and reliability coefficients

Datasets

WageData

Earnings data from the Panel Study of Income Dynamics

teen_poverty

National Longitudinal Survey of Youth teenage women poverty data

nlsy

National Longitudinal Survey of Youth data