Title: Read Hierarchical Fixed Width Files
person("Greg", "Freedman Ellis", , "gfellis@umn.edu", c("aut", "cre")),
person("Hadley", "Wickham", , "hadley@rstudio.com", "ctb", comment = "Code adapted from readr"),
person("Jim", "Hester", , "james.hester@rstudio.com", c("ctb"), comment = "Code adapted from readr"),
person("Romain", "Francois", role = "ctb", comment = "Code adapted from readr"),
person("R Core Team", role = "ctb", comment = "Code adapted from readr"),
person("RStudio", role = c("cph", "fnd"), comment = "Code adapted from readr"),
person("Jukka", "Jylänki", role = c("ctb", "cph"), comment = "Code adapted from readr"),
person("Mikkel", "Jørgensen", role = c("ctb", "cph"), comment = "Code adapted from readr"),
person("University of Minnesota", role = "cph"))
Maintainer: Greg Freedman Ellis <gfellis@umn.edu>
Description: Read hierarchical fixed width files like those commonly used by
many census data providers. Also allows for reading of data in chunks,
and reading 'gzipped' files without storing the full file in memory.
Hadley Wickham [ctb] (Code adapted from readr),
Jim Hester [ctb] (Code adapted from readr),
Romain Francois [ctb] (Code adapted from readr),
R Core Team [ctb] (Code adapted from readr),
RStudio [cph, fnd] (Code adapted from readr),
Jukka Jylänki [ctb, cph] (Code adapted from readr),
Mikkel Jørgensen [ctb, cph] (Code adapted from readr),

person("Greg", "Freedman Ellis", role = "aut"),
person("Derek Burk", email = "ipums+cran@umn.edu", role = c("aut", "cre")),

Title: Hierarchical Semiparametric Regression of Test Statistics
Author: Long Qu <longor@iastate.edu>, Dan Nettleton
Depends: R (>= 2.5.1), Matrix , Iso(>= 0.0-5), splines, fda, MASS
Suggests: multtest, qvalue, ROCR, rgl, pi0
Description: This package implements methods for hierarchical
semiparametric regression models for test statistics.
Specifically, test statistics given the null/alternative
hypotheses are modeled parametrically, whereas the unobservable
status of null/alternative hypotheses are modeled using
nonparametric addtive logistic regression over covariates.