Differential geometric least angle regression method for fitting sparse generalized linear models. In this version of the package, the user can fit models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. Furthermore, several link functions can be used to model the relationship between the conditional expected value of the response variable and the linear predictor. The solution curve can be computed using an efficient predictor-corrector or a cyclic coordinate descent algorithm, as described in the paper linked to via the URL below.

c(person(given = "Luigi", family = "Augugliaro", email = "luigi.augugliaro@unipa.it", role = c("aut", "cre")),
person(given = "Angelo", family = "Mineo", role = "aut"),
person(given = "Ernst", family = "Wit", role = "aut"),
person(given = "Hassan", family = "Pazira", role = "aut"),
person(given = "Michael", family = "Wichura", role = c("ctb", "cph")),
person(given = "John", family = "Burkardt", role = c("ctb", "cph")))
Luigi Augugliaro [aut, cre],
Angelo Mineo [aut],
Ernst Wit [aut],
Hassan Pazira [aut],
Michael Wichura [ctb, cph],
John Burkardt [ctb, cph]

Peter K Dunn <dunn@usq.edu.au> and Gordon K Smyth
Fitting double  generalized linear models

Peter K Dunn <pdunn2@usc.edu.au> and Gordon K Smyth
Fitting double generalized linear models

Model fitting and evaluation tools for double generalized linear models (DGLMs).
This class of models uses one generalized linear model (GLM) to fit the specified response and
a second GLM to fit the deviance of the first model.

Model fitting and evaluation tools for double generalized linear
models (DGLMs). This class of models uses one generalized linear model (GLM)
to fit the specified response and a second GLM to fit the deviance of the first
model.

dgmb Simulating data for PLS structural models
Alba Martinez-Ruiz <amartine@ucsc.cl> and Claudia
Martinez-Araneda <cmartinez@ucsc.cl>
Random data generation for PLS structural models.

Alba Martinez-Ruiz <amartine@ucsc.cl> and Claudia Martinez-Araneda <cmartinez@ucsc.cl>