Title: Fitting the copCAR regression model for discrete areal data
Description: copCAR provides tools for fitting the copCAR regression model for discrete areal data. Three types of estimation are supported: continuous extension, composite marginal likelihood, and distributional transform.
Packaged: 2014-09-06 20:00:02 UTC; jphughesjr

areal data. Three types of estimation are supported (continuous
extension, composite marginal likelihood, and distributional transform),
for three types of outcomes (Bernoulli, negative binomial, and Poisson).

Packaged: 2020-05-07 16:49:00 UTC; jphughesjr

Packaged: 2021-01-08 14:37:57 UTC; jphughesjr

Title: Statistical methods to model and adjust for bias in meta-analysis
Author: James Carpenter <James.Carpenter@lshtm.ac.uk>, Guido Schwarzer <sc@imbi.uni-freiburg.de>

Title: Statistical methods to model and adjust for bias in
Author: James Carpenter <James.Carpenter@lshtm.ac.uk>, Guido Schwarzer

Title: Coverage Probability Excursion (CoPE) Sets
Description: Provides functions to  compute and plot Coverage
for real valued functions on a 2-dimensional domain. CoPE sets are obtained
from repeated noisy observations of the function on the entire domain.
They are designed to bound the excursion
set of the target function at a given level from above and below with
function can be a parameter in spatially-indexed linear regression.
Packaged: 2015-01-27 05:59:50 UTC; msommerfeld

Support by NIH grant R01 CA157528 is gratefully acknowledged.
Packaged: 2017-02-13 10:21:10 UTC; msommerfeld

Authors@R: c(person("MA Jian", role = c("aut", "cre"),
Description: The nonparametric method for estimating copula entropy is implemented. The method composes of two simple steps: estimating empirical copula by rank statistic and estimating copula entropy with k-Nearest-Neighbour method. Copula Entropy is a mathematical concept for multivariate statistical independence measuring and testing, and proved to be equivalent to mutual information. Estimating copula entropy can be applied to many cases, including but not limited to variable selection and causal discovery (by estimating transfer entropy). Please refer to Ma and Sun (2011) <doi: 10.1016/S1007-0214(11)70008-6> for more information.