Title: Cystic Fibrosis Survival Prediction Model Based on Stanojevic
person("Sanja", "Stanojevic", email = "sanja.stanojevic@sickkids.ca", role = c("aut", "cph")),
person("Jenna", "Sykes", email = "SykesJ@smh.ca", role = c("aut", "cph")),
person("George A.", "Whitmore", email = "george.whitmore@mcgill.ca", role = c("aut", "cph")),
person("Shawn D.", "Aaron", email = "saaron@toh.ca", role = c("aut", "cph")),
person("Aida", "Kazemi", email = "aidakazemi10@gmail.com", role = c("aut")),
person("Amin", "Adibi", email = "adibi@alumni.ubc.ca", role = c("aut", "cre")))
Description: Allows clinicians to predict survival probabilities over the next two years for cystic fibrosis patients, based on the clinical prediction models published in Stanojevic et al. (2019) <doi:10.1183/13993003.00224-2019>.

Title: Forward Selection using Concordance/C-Index
Description: Performs forward model selection, using the C-index/concordance
Imports: survival, dplyr, stats, magrittr, tibble
Packaged: 2021-03-25 16:00:24 UTC; johnmuschelli
Author: John Muschelli [aut, cre] (<https://orcid.org/0000-0001-6469-1750>),

Description: Developed as a collaboration between Earth lab and the North Central Climate Adaptation Science Center to help users gain insights from available climate data. Includes tools and instructions for downloading climate data via a 'USGS' API and then organizing those data for visualization and analysis that drive insight. Web interface for 'USGS' API can be found at <http://thredds.northwestknowledge.net:8080/thredds/reacch_climate_CMIP5_aggregated_macav2_catalog.html>.
Depends: R (>= 3.5.0), plyr, dplyr (>= 1.0.10), osmdata, magrittr
Imports: tidync, future, furrr, sf, epitools, tidyr, rlang, pipeR,
Suggests: lubridate, testthat (>= 3.0.0), knitr

Author: Bill Pikounis and John Oleynick <cg@billpikounis.net>
Maintainer: Bill Pikounis and John Oleynick <cg@billpikounis.net>
Description: cg is comprehensive data analysis software, and stands for
"compare groups." Its genesis and evolution are driven by
common needs to compare administrations, conditions, etc. in
medicine research & development. The current version provides
comparisons of unpaired samples, i.e. a linear model with one
factor of at least two levels. Good data graphs, modern
statistical methods, and useful displays of results are
Depends: R (>= 2.12.0), Hmisc, base, utils, methods, stats, graphics,
grid, lattice, MASS, survival, multcomp, nlme