Predicts the most common race of a surname and based on U.S. Census
    data, and the most common first named based on U.S. Social Security Administration data.

Methods for spatial predictive modeling, especially for spatial distribution models. This includes algorithms for model fitting and prediction, as well as methods for model evaluation.

Implements conformal prediction methods for constructing
    prediction intervals (regression) and prediction sets (classification)
    with finite-sample coverage guarantees. Methods include split conformal,
    'CV+' and 'Jackknife+' (Barber et al. 2021) <doi:10.1214/20-AOS1965>,
    'Conformalized Quantile Regression' (Romano et al. 2019)
    <doi:10.48550/arXiv.1905.03222>, 'Adaptive Prediction Sets'
    (Romano, Sesia, Candes 2020) <doi:10.48550/arXiv.2006.02544>,
    'Regularized Adaptive Prediction Sets' (Angelopoulos et al. 2021)
    <doi:10.48550/arXiv.2009.14193>, Mondrian conformal prediction for
    group-conditional coverage (Vovk et al. 2005), weighted conformal
    prediction for covariate shift (Tibshirani et al. 2019), and adaptive
    conformal inference for sequential prediction (Gibbs and Candes 2021).
    All methods are distribution-free and provide calibrated uncertainty
    quantification without parametric assumptions. Works with any model that can
    produce predictions from new data, including 'lm', 'glm', 'ranger',
    'xgboost', and custom user-defined models.

Fetches the 'PREDICTS' database and relevant metadata from the Data
    Portal at the Natural History Museum, London <https://data.nhm.ac.uk>. Data
    were collated from over 400 existing spatial comparisons of local-scale
    biodiversity exposed to different intensities and types of anthropogenic
    pressures, from sites around the world. These data are described in Hudson
    et al. (2013) <doi:10.1002/ece3.2579>.

Methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.