Centers of population (centroid) data for census areas in the
United States.

A Companion Package to the Book "U-Statistics, M-Estimation and
Resampling"
A set of functions leading to multivariate response L1 regression.
This includes functions on computing Euclidean inner products and norms,
weighted least squares estimates on multivariate responses, function to compute
fitted values and residuals. This package is a companion to the book "U-Statistics,
M-estimation and Resampling", by Arup Bose and Snigdhansu Chatterjee, to appear
in 2017 as part of the "Texts and Readings in Mathematics" (TRIM) series of
Hindustan Book Agency and Springer-Verlag.

Unbinned weighted histogram analysis method (UWHAM)
A method for estimating log-normalizing constants (or free
energies) and expectations from multiple distributions (such as
multiple generalized ensembles).

Unbinned Weighted Histogram Analysis Method (UWHAM)

Hans Laevens, Luc Duchateau, Klara Goethals, Ewoud De Troyer and Paul Janssen
The udder quarter infection data set contains infection times of individual cow udder quarters with Corynebacterium bovis (Laevens et al. 1997 <DOI:10.3168/jds.S0022-0302(97)76295-7>). Obviously, the four udder quarters are clustered within a cow, and udder quarters are sampled only approximately monthly, generating interval-censored data. The data set contains both covariates that change within a cow (e.g., front and rear udder quarters) and covariates that change between cows (e.g., parity [the number of previous calvings]). The correlation between udder infection times within a cow also is of interest, because this is a measure of the infectivity of the agent causing the disease. Various models have been applied to address the problem of interdependence for right-censored event times. These models, as applied to this data set, can be found back in the publications found in the reference list.

Bayesian Analysis of a Non-Informative Parametrization for
Gaussian Mixture Distributions
coda, gtools, graphics, grDevices, stats
A generic reference Bayesian analysis of unidimensional mixtures of Gaussian distributions obtained by a location-scale parameterisation of the model is implemented. Included functions can be applied to produce a Bayesian analysis of Gaussian mixtures with an arbitrary number of components, with no need to define the prior distribution.