Multivariate Correlation Estimator and Statistical Inference Procedures.
Multivariate correlation estimation and statistical inference. See package vignette.

The package provides multivariate correlation estmation and sets of statsitical inference procedures.

Multivariate Correlation Estimator and Statistical Inference
Procedures.
Multivariate correlation estimation and statistical
inference. See package vignette.

To identify the globally most discriminative subnetwork
from gene expression profiles using an optimization model and
genetic algorithm

To identify the globally most discriminative subnetwork
from gene expression profiles and protein-protein interactions
data using an optimization method and genetic algorithm

To identify the globally most discriminative subnetwork from gene
expression profiles using an optimization model and genetic algorithm

Copula-Based Semiparametric Models for Spatio-Temporal Data
Parameter estimation, one-step ahead forecast and new location
prediction methods for spatio-temporal data.

Functions, data and code for count data.
Joseph M Hilbe <hilbe@asu.edu>, Andrew Robinson
<apro@unimelb.edu.au>
Functions, data and code for Hilbe, J.M. 2009. Negative
Binomial Regression, 2nd Edition (Cambridge University Press)

Andrew Robinson <apro@unimelb.edu.au>, Joseph M Hilbe
<hilbe@asu.edu>

Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2-nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).

Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).

A Residue-Residue Contact Detecting Method
Reda Rawi [aut,cre], Matyas A. Sustik [aut], Ben Calderhead [aut]
Contact prediction using shrinked covariance (COUSCOus). COUSCOus is a residue-residue contact detecting method approaching the contact inference using the glassofast implementation of Matyas and Sustik (2012, The University of Texas at Austin UTCS Technical Report 2012:1-3. TR-12-29.) that solves the L_1 regularised Gaussian maximum likelihood estimation of the inverse of a covariance matrix. Prior to the inverse covariance matrix estimation we utilise a covariance matrix shrinkage approach, the empirical Bayes covariance estimator, which has been shown by Haff (1980) <DOI:10.1214/aos/1176345010> to be the best estimator in a Bayesian framework, especially dominating estimators of the form aS, such as the smoothed covariance estimator applied in a related contact inference technique PSICOV.