c(
person("Jeffrey", "Horner", role = c("aut", "cph")),
person("Greg", "Hunt", , "greg@firmansyah.com", role = c("aut", "cre", "cph"))
)
Implements a templating framework for mixing text and R code
for report generation. brew template syntax is similar to PHP, Ruby's
erb module, Java Server Pages, and Python's psp module.

Extracting Usable Data from the Grad Cafe Results Search
Retrieves and parses graduate admissions survey data from the Grad Cafe website (http://thegradcafe.com).

Fit binomial-response GLMs using either a modified-score approach to bias-reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. Fitting takes place by iteratively fitting a local GLM on a pseudo-data representation. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the bias-reduced estimates.

Bias reduction in binomial-response GLMs.
Fit binomial-response GLMs using either a modified-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. Fitting takes place by iteratively fitting a local GLM on a pseudo-data representation. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the bias-reduced estimates.

Fit binomial-response GLMs using either a modified-score
approach to bias reduction or maximum penalized likelihood
where penalization is by Jeffreys invariant prior. Fitting
takes place by iteratively fitting a local GLM on a pseudo-data
representation. The interface is essentially the same as 'glm'.
More flexibility is provided by the fact that custom
pseudo-data representations can be specified and used for model
fitting. Functions are provided for the construction of
confidence intervals for the bias-reduced estimates.