A collection of functions to perform the Application
        Programming Interface (API) calls associated with the Walk
        Score website (www.walkscore.com) within the R environment.
        These functions can be used to query the Walk Score and Transit
        Score database for a wide variety of information using R
        scripts. This package includes the simple Walk Score and
        Transit Score API calls, which return the scores associated
        with an input location, as well as calls which return some data
        used to calculate the scores. These functions are especially
        useful for mass data collection and gathering Walk Score and
        Transit Score values for large lists of locations.

The 'shiny' application Wallace is a modular platform for 
    reproducible modeling of species niches and distributions. Wallace
    guides users through a complete analysis, from the acquisition of species
    occurrence and environmental data to visualizing model predictions on an 
    interactive map, thus bundling complex workflows into a single, 
    streamlined interface. An extensive vignette, which guides users through 
    most package functionality can be found on the package's GitHub Pages 
    website: <https://wallaceecomod.github.io/wallace/articles/tutorial-v2.html>.

Datasets from the WallOmics project. Contains phenomics, metabolomics, proteomics and transcriptomics data collected from two organs of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. Exploratory and integrative analyses of these data are presented in Durufle et al (2020) <doi:10.1093/bib/bbaa166> and Durufle et al (2020) <doi:10.3390/cells9102249>.

A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events.