Performs forward model selection, using the C-index/concordance
  in survival analysis models.

Estimate the severity of a disease and ascertainment of
    cases, as discussed in Nishiura et al. (2009)
    <doi:10.1371/journal.pone.0006852>.

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>.

Support for all calendars as specified in the Climate and Forecast 
    (CF) Metadata Conventions for climate and forecasting data. The CF Metadata 
    Conventions is widely used for distributing files with climate observations 
    or projections, including the Coupled Model Intercomparison Project (CMIP) 
    data used by climate change scientists and the Intergovernmental Panel on
    Climate Change (IPCC). This package specifically allows the user to work 
    with any of the CF-compliant calendars (many of which are not compliant with 
    POSIXt). The CF time coordinate is formally defined in the CF Metadata 
    Conventions document available at <https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#time-coordinate>.

Comprehensive data analysis software, and the name "cg" stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research and development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. It also provides comparisons of two paired samples. Good data graphs, modern statistical methods, and useful displays of results are emphasized.

Fits constrained groupwise additive index models and provides functions for inference and interpretation of these models. The method is described in Masselot, Chebana, Campagna, Lavigne, Ouarda, Gosselin (2022) "Constrained groupwise additive index models" <doi:10.1093/biostatistics/kxac023>.