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.

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.

Methods to help selecting General Circulation Models (GCMs) 
    in the context of projecting models to future scenarios. It is provided
    clusterization algorithms, distance and correlation metrics, as well as
    a tailor-made algorithm to detect the optimum subset of GCMs that recreate
    the environment of all GCMs as proposed in Esser et al. (2025) <doi:10.1111/gcb.70008>.

One way to choose the number of principal components is via the reconstruction error. This package is designed mainly for this purpose. Graphical representation is also supported, plus some other principal component analysis related functions. References include: Jolliffe I.T. (2002). Principal Component Analysis. <doi:10.1007/b98835> and Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press.