# Yves Croissant

#### 11 packages on CRAN

A toolbox for descriptive statistics, based on the computation of frequency and contingency tables. Several statistical functions and plot methods are provided to describe univariate or bivariate distributions of factors, integer series and numerical series either provided as individual values or as bins.

Provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure.

Estimation of models with zero left-censored variables. Null values may be caused by a selection process (Cragg (1971) <doi:10.2307/1909582>), insufficient resources (Tobin (1958) <doi:10.2307/1907382>) or infrequency of purchase (Deaton and Irish (1984) <doi:10.1016/0047-2727(84)90067-7>).

Maximum likelihood estimation of random utility discrete choice models. The software is described in Croissant (2020) <doi:10.18637/jss.v095.i11> and the underlying methods in Train (2009) <doi:10.1017/CBO9780511805271>.

Estimation of panel models for glm-like models: this includes binomial models (logit and probit) count models (poisson and negbin) and ordered models (logit and probit), as described in Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7, Hsiao (2014) Analysis of Panel Data <doi:10.1017/CBO9781139839327> and Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4.

A set of estimators and tests for panel data econometrics, as described in Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7, Hsiao (2014) Analysis of Panel Data <doi:10.1017/CBO9781139839327> and Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4.

Infrastructure for extended formulas with multiple parts on the right-hand side and/or multiple responses on the left-hand side (see <doi:10.18637/jss.v034.i01>).

An implementation of maximum simulated likelihood method for the estimation of multinomial logit models with random coefficients as presented by Sarrias and Daziano (2017) <doi:10.18637/jss.v079.i02>. Specifically, it allows estimating models with continuous heterogeneity such as the mixed multinomial logit and the generalized multinomial logit. It also allows estimating models with discrete heterogeneity such as the latent class and the mixed-mixed multinomial logit model.