Title: Fitting l2-regularized logit choice models via generalized
Description: Fits linear discrete logic choice models with l2 regularization. To handle reasonably sized datasets, we employ an accelerated version of generalized gradient descent.

Description: Fits linear discrete logit choice models with l2 regularization. To handle reasonably sized datasets, we employ an accelerated version of generalized gradient descent.

Title: Functions for the logitnormal distribution.
Description: Density, distribution, quantile and random generation
function for the logitnormal distribution. Estimation of the
mode and the first two moments. Estimation of distribution

Title: Functions for the Logitnormal Distribution
Description: Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.
Suggests: RUnit, knitr, ggplot2, reshape2

Suggests: RUnit, knitr, markdown, ggplot2, reshape2

Title: Logit Models w/Preference & WTP Space Utility Parameterizations
Description: 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. An option is available to run a 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 simulating the expected shares of a set of alternatives using an estimated model. MXL models assume uncorrelated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) "Discrete Choice Methods with Simulation, 2nd Edition" <doi:10.1017/CBO9780511805271>.
Suggests: knitr, rmarkdown, here, ggplot2
Imports: nloptr, stats, randtoolbox, MASS