In nestedLogit: Nested Dichotomy Logistic Regression Models

As befits a model-fitting function, the package defines a nearly complete set of methods for "nestedLogit" objects:

print() and summary() print the results for each of the submodels.
update() re-fits the model, allowing changes to the model formula, data, subset, and contrasts arguments.
coef() returns the coefficients for the predictors in each dichotomy.
vcov() returns the variance-covariance matrix of the coefficients
predict() computes predicted probabilities for the response categories, either for the cases in the data, which is equivalent to fitted(), or for arbitrary combinations of the predictors; the latter is useful for producing plots to aid interpretation.
confint() calculates confidence intervals for the predicted probabilities or predicted logits.
as.data.frame() method for predicted probabilities and logits converts these to long format for use with ggplot2.
glance() and tidy() are extensions of broom::glance.glm() and broom::tidy.glm() to obtain compact summaries of a "nestedLogit" model object.
plot() provides basic plots of the predicted probabilities over a range of values of the predictor variables.
models() is an extractor function for the binary logit models in the "nestedLogit" object
Effect() calculates marginal effects collapsed over some variable(s) for the purpose of making effect plots.

These functions are supplemented by various methods for testing hypotheses about and comparing nested-logit models:

anova() provides analysis-of-deviance Type I (sequential) tests for each dichotomy and for the combined model. When given a sequence of model objects, anova() tests the models against one another in the order specified.
Anova() uses car::Anova() to provide analysis-of-deviance Type II or III (partial) tests for each dichotomy and for the combined model.
linearHypothesis() uses car::linearHypothesis() to compute Wald tests for hypotheses about coefficients or their linear combinations.
logLik() returns the log-likelihood and degrees of freedom for the nested-dichotomies logit model.
Through logLik(), the AIC() and BIC() functions compute the Akaike and Bayesian information criteria model-comparison statistics.