Fitting the copCAR Regression Model for Discrete Areal Data
Emily Goren <emily.goren@gmail.com> and John Hughes <hughesj@umn.edu>
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.

Emily Goren <emily.goren@gmail.com> and John Hughes <jphughesjr@gmail.com>

Emily Goren <emily.goren@gmail.com> and John Hughes
<jphughesjr@gmail.com>
Provides tools for fitting the copCAR (Hughes, 2015)
<DOI:10.1080/10618600.2014.948178> regression model for discrete
areal data. Three types of estimation are supported: continuous
extension, composite marginal likelihood, and distributional transform.

Fitting the copCAR regression model for discrete areal data
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.

Provides tools for fitting the copCAR (Hughes, 2015)
<DOI:10.1080/10618600.2014.948178> regression model for discrete
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).

Emily Goren <emily.goren@gmail.com> and John Hughes
<drjphughesjr@gmail.com>

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

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

Coverage Probability Excursion (CoPE) Sets
person("Max", "Sommerfeld",
email = "max.sommerfeld@mathematik.uni-goettingen.de",
role = c("aut", "cre"))
Provides functions to  compute and plot Coverage
Probability Excursion (CoPE) sets
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
a predefined probability. The target
function can be a parameter in spatially-indexed linear regression.