Title: Isotonic Proportional Hazards Model
Author: Yunro Chung, Anastasia Ivanova, Michael G. Hudgens and Jason P. Fine
Description: Nonparametric estimation of an isotonic covariate effect for Cox's partial likelihood.

Author: Yunro Chung [cre], Anastasia Ivanova, Michael G. Hudgens and Jason P. Fine

Description: Nonparametric estimation of an isotonic covariate effect for proprotional hazards model.

Description: Nonparametric estimation of an isotonic covariate effect for proportional hazards model.

Description: Interface to the raw data file formats commonly encountered in scientific disciplines that make use of stable isotopes.
Authors@R: person("Sebastian", "Kopf", email = "sebastian.kopf@colorado.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2044-0201"))
Imports: methods, R.utils, magrittr, rlang (>= 0.4.5), tidyselect (>=
tidyr (>= 1.0.0), glue, stringr, purrr, future, lubridate,
readr, progress, xml2 (>= 1.3.1), rhdf5, feather, UNF, openxlsx
Suggests: testthat, readxl, knitr, rmarkdown, covr
Author: Sebastian Kopf [aut, cre] (<https://orcid.org/0000-0002-2044-0201>)

given = "Brett", family = "Davidheiser-Kroll",
Suggests: devtools, testthat, feather (>= 0.3.5), readxl (>= 1.3.1),
Author: Sebastian Kopf [aut, cre] (<https://orcid.org/0000-0002-2044-0201>),

Title: Active set and generalized PAVA for isotone optimization
Description: This package contains two main functions: gpava() for
solving general isotone regression problem using the
pool-adjacent-violators algorithm (PAVA). activeSet() provides
a framwork of active set methods for isotone optimization
problems. Various loss functions are pre-spcified.
Packaged: Wed Mar 25 05:17:32 2009; rforge

a framework of active set methods for isotone optimization

problems with arbitrary order restrictions. Various types of

"mair@fas.harvard.edu"), person("Jan", "De Leeuw", role = "aut"),
Author: Patrick Mair [aut, cre], Jan De Leeuw [aut], Kurt Hornik [aut]
Description: Contains two main functions: gpava() for

Title: Active Set and Generalized PAVA for Isotone Optimization
Description: Contains two main functions: one for
solving general isotone regression problems using the
pool-adjacent-violators algorithm (PAVA); another one provides
a framework for active set methods for isotone optimization