Julie Tibshirani [aut, cre],
Susan Athey [aut],
Stefan Wager [aut],
Rina Friedberg [ctb],
Luke Miner [ctb],
Marvin Wright [ctb]
A pluggable package for forest-based statistical estimation and inference.
GRF currently provides methods for non-parametric least-squares regression,
quantile regression, and treatment effect estimation (optionally using instrumental
variables). This package is currently in beta, and we expect to make continual
improvements to its performance and usability.
DiceKriging, Matrix, methods, Rcpp (>= 0.12.15), sandwich (>=
2.4-0)

DiagrammeR, DiceKriging, lmtest, Matrix, methods, Rcpp (>=
0.12.15), sandwich (>= 2.4-0)

Julie Tibshirani [aut, cre],
Susan Athey [aut],
Rina Friedberg [ctb],
Vitor Hadad [ctb],
Luke Miner [ctb],
Stefan Wager [aut],
Marvin Wright [ctb]
DiceKriging, lmtest, Matrix, methods, Rcpp (>= 0.12.15),
sandwich (>= 2.4-0)

c(
person("Julie", "Tibshirani", role = c("aut", "cre"), email = "jtibs@cs.stanford.edu"),
person("Susan", "Athey", role = "aut"),
person("Rina", "Friedberg", role = "ctb"),
person("Vitor", "Hadad", role = "ctb"),
person("David", "Hirshberg", role = "ctb"),
person("Luke", "Miner", role = "ctb"),
person("Erik", "Sverdrup", role = "ctb"),
person("Stefan", "Wager", role = "aut"),
person("Marvin", "Wright", role = "ctb")
)
A pluggable package for forest-based statistical estimation and inference.
GRF currently provides methods for non-parametric least-squares regression,
quantile regression, and treatment effect estimation (optionally using instrumental
variables).
Julie Tibshirani [aut, cre],
Susan Athey [aut],
Rina Friedberg [ctb],
Vitor Hadad [ctb],
David Hirshberg [ctb],
Luke Miner [ctb],
Erik Sverdrup [ctb],
Stefan Wager [aut],
Marvin Wright [ctb]

Julie Tibshirani [aut, cre],
Susan Athey [aut],
Stefan Wager [aut],
Marvin Wright [ctb],
all contributors to the included version of Eigen [ctb]

Julie Tibshirani [aut, cre],
Susan Athey [aut],
Stefan Wager [aut],
Marvin Wright [ctb]

A pluggable package for forest-based statistical estimation and inference.
GRF currently provides methods for non-parametric least-squares regression,
quantile regression, survival regression and treatment effect estimation (optionally using instrumental
variables), with support for missing values.