For details see Niku et al. (2019a) <doi:10.1371/journal.pone.0216129> and
Imports: MASS, Matrix, statmod, fishMod, mgcv
Suggests: rmarkdown, testthat, gclus, corrplot, lattice

Title: Distributed 'glm' for Big Data using 'ddR' API
Description: Distributed training and prediction of generalized linear models using 'ddR' (Distributed Data Structures) API in the 'ddR' package.

Packaged: 2017-02-28 17:37:11 UTC; indrajitroy

Title: 'C' and 'Java' Source Code Generator for Fitted Glm Objects
Authors@R: c(person("Oscar","Castro-Lopez", email="castroloj@gmail.com", role=c("cre", "aut")),
Description: Provides two functions that generate source code implementing the predict
function of fitted glm objects. In this version, code can be generated for either 'C'
or 'Java'. The idea is to provide a tool for the easy and fast deployment of glm
predictive models into production. The source code generated by this package implements
two function/methods. One of such functions implements the equivalent to
predict(type="response"), while the second implements predict(type="link"). Source code
is written to disk as a .c or .java file in the specified path. In the case of c, an .h

Title: Predicted Values and Discrete Changes for GLM
Description: Functions to calculate predicted values and the difference between the two cases with confidence interval for glm, glm.nb, polr and multinom.
Depends: R (>= 3.1.0), stats, MASS, iterpc

Description: Functions to calculate predicted values and the difference between
the two cases with confidence interval for glm(), glm.nb(), polr() and multinom().

Depends: R (>= 3.1.0), stats, MASS, parallel
Packaged: 2017-12-06 14:42:27 UTC; Benjamin Schlegel

Packaged: 2018-05-03 11:01:05 UTC; Benjamin Schlegel

the two cases with confidence interval for lm() [linear model], glm() [general linear model], glm.nb() [negative binomial model],
polr() [ordinal logistic model] and multinom() [multinomial model] using Monte Carlo simulations. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.
Packaged: 2019-09-04 11:56:49 UTC; Benjamin Schlegel