Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) <doi:10.1017/CBO9780511805271>. More details can be found in Helveston (2023) <doi:10.18637/jss.v105.i10>.

Computes log-transformed kernel density estimates for positive data using a variety of kernels. It follows the methods described in Jones, Nguyen and McLachlan (2018) <doi:10.21105/joss.00870>.

Functions to sample from the double log normal distribution and calculate the density, distribution and quantile functions.

Functions to fit log-multiplicative models using 'gnm', with
  support for convenient printing, plots, and jackknife/bootstrap
  standard errors. For complex survey data, models can be fitted from
  design objects from the 'survey' package. Currently supported models
  include UNIDIFF (Erikson & Goldthorpe, 1992),
  a.k.a. log-multiplicative layer effect model (Xie, 1992)
  <doi:10.2307/2096242>, and several association models:
  Goodman (1979) <doi:10.2307/2286971>
  row-column association models of the RC(M) and RC(M)-L families
  with one or several dimensions; two skew-symmetric association
  models proposed by Yamaguchi (1990) <doi:10.2307/271086>
  and by van der Heijden & Mooijaart (1995) <doi:10.1177/0049124195024001002>
  Functions allow computing the intrinsic association coefficient
  (see Bouchet-Valat (2022) <doi:10.1177/0049124119852389>)
  and the Altham (1970) index <doi:10.1111/j.2517-6161.1970.tb00816.x>,
  including via the Bayes shrinkage estimator proposed
  by Zhou (2015) <doi:10.1177/0081175015570097>;
  and the RAS/IPF/Deming-Stephan algorithm.