R (>= 2.10), rpart (>= 3.1-8), MASS, mlbench, survival, nnet,
class

R (>= 2.10), rpart (>= 3.1-8), MASS, mlbench, survival, nnet,
class, prodlim

R (>= 2.10), rpart (>= 3.1-8), MASS, survival, nnet, class,
prodlim

c(person("Andrea", "Peters", role = "aut"),
person("Torsten", "Hothorn", role = c("aut", "cre"),
email = "Torsten.Hothorn@R-project.org"),
person("Brian D.", "Ripley", role = "ctb"),
person("Terry", "Therneau", role = "ctb"),
person("Beth", "Atkinson", role = "ctb"))
rpart (>= 3.1-8), MASS, survival, nnet, class, prodlim
Andrea Peters [aut],
Torsten Hothorn [aut, cre],
Brian D. Ripley [ctb],
Terry Therneau [ctb],
Beth Atkinson [ctb]

mvtnorm, mlbench, TH.data, randomForest, party

In a linear regression setting, priors can be assigned to the regression function using a vector space framework, and the posterior estimate of the regression function obtained. I-priors are a class of such priors based on the principle of maximum entropy. While the main interest of I-prior modelling is prediction, inference is also possible, e.g. via log-likelihood ratio tests. This package supports both formula and non-formula based input. Estimation is mainly done via an EM algorithm, but there is flexibility in using any R optimiser to maximise the log-likelihood computed by the kernel loader function.

person("Haziq", "Jamil", email = "haziq.jamil@gmail.com",
role = c("aut", "cre"))
Provides methods to perform and analyse I-prior regression models.
Estimation is mainly done via an EM algorithm, but there is flexibility in
using any optimiser.

Provides methods to perform and analyse I-prior regression models.
Estimation is done either via direct optimisation of the log-likelihood or
an EM algorithm.
doSNOW, foreach, ggplot2, mvtnorm, Rcpp (>= 0.12.5), reshape2,
scales

caret, knitr, MASS, R.rsp, rmarkdown, testthat

Interfaces to Phylogenetic Software in R
This package provides functions that wrap popular phylogenetic software for sequence alignment, masking of sequence alignments, and estimation of phylogenies and ancestral character states.