Offers a set of objects tailored to simplify working with choice 
    data. It enables the computation of choice probabilities and the 
    likelihood of various types of choice models based on given data.

Design functions for DCMs and other types of choice studies (including MaxDiff and other tradeoffs).

Implements an MCMC algorithm to estimate a hierarchical multinomial logit model with a normal heterogeneity distribution. The algorithm uses a hybrid Gibbs Sampler with a random walk metropolis step for the MNL coefficients for each unit. Dependent variable may be discrete or continuous. Independent variables may be discrete or continuous with optional order constraints. Means of the distribution of heterogeneity can optionally be modeled as a linear function of unit characteristics variables.

Fast estimation of discrete-choice models for applied economics.
    Frequentist likelihoods, analytical gradients, and Hessians are implemented
    in C++ with 'OpenMP' parallelism, scaling efficiently to specifications with
    many alternative-specific constants. Compiled Gibbs samplers provide Bayesian
    multinomial probit and hierarchical models. Post-estimation routines cover
    predicted shares, own- and cross-price elasticities, diversion ratios,
    willingness to pay, and welfare counterfactuals. Supports multinomial logit
    ('MNL'), mixed logit ('MXL'), nested logit ('NL'), Bayesian multinomial probit
    ('MNP'), and hierarchical Bayesian multinomial logit and probit ('HMNL',
    'HMNP').

Collection of utility functions for visualizing
    body map data collected with the Collaborative Health Outcomes
    Information Registry.

Amends errors, augments data and aids analysis of John Snow's map
  of the 1854 London cholera outbreak.

Sampling from the Cholesky factorization of a Wishart random 
    variable, sampling from the inverse Wishart distribution, sampling from 
    the Cholesky factorization of an inverse Wishart random variable, sampling
    from the pseudo Wishart distribution, sampling from the generalized
    inverse Wishart distribution, computing densities for the Wishart 
    and inverse Wishart distributions, and computing the multivariate gamma 
    and digamma functions. Provides a header file so the C functions can be
    called directly from other programs.