LF06art                 Data to replicate Long and Freese's (2006)
                        count models (pp354-414)
LF06travel              Travel time example data for
                        alternative-specific outcomes.
Mize19AH                Add-Health Data analzed in Mize (2019)
Mize19GSS               General Social Survey Data analzed in Mize
                        (2019)
compare.margins         Compares two marginal effects (MEMs or AMEs).
                        Estimate of uncertainty is from a simulated
                        draw from a normal distribution.
count.fit               Fits four different count models and compares
                        them.
diagn                   Computes diagnostics for generalized linear
                        models.
ess                     A subset of data from the European Social
                        Survey
essUK                   A subset of data from the European Social
                        Survey
first.diff.fitted       Computes the first difference in fitted values,
                        or a series of first differences. Inference in
                        supported via the delta method or
                        bootstrapping.
gss2016                 Data from the 2016 General Social Survey.
list.coef               Transform glm and mixed model objects into
                        model summaries that include coefficients,
                        standard errors, exponentiated coefficients,
                        confidence intervals and percent change.
logan                   Replication data for Logan's (1983) application
                        of conditional logistic regression to mobility
                        processes.
margins.dat             Add model predictions, standard errors and
                        confidence intervals to a design matrix for a
                        model object.
margins.dat.clogit      Computes predicted probabilities for
                        conditional and rank-order/exploded logistic
                        regression models. Inference is based upon
                        simulation techniques (requires the MASS
                        package). Alternatively, bootstrapping is an
                        option for conditional logistic regression
                        models.
margins.des             Creates a design matrix of idealized data for
                        illustrating model predictions.
rubins.rule             Aggregate Standard Errors using Rubin's Rule.
second.diff.fitted      Computes the second difference in fitted
                        values. Inference in supported via the delta
                        method or bootstrapping.
wagepan                 Data to illustrate mixed effects regression
                        models with serial correlation.
