The Pearson-ICA algorithm is a mutual information-based
        method for blind separation of statistically independent source
        signals. It has been shown that the minimization of mutual
        information leads to iterative use of score functions, i.e.
        derivatives of log densities. The Pearson system allows
        adaptive modeling of score functions. The flexibility of the
        Pearson system makes it possible to model a wide range of
        source distributions including asymmetric distributions. The
        algorithm is designed especially for problems with asymmetric
        sources but it works for symmetric sources as well.

Validation of risk predictions obtained from survival models and
    competing risk models based on censored data using inverse weighting and
    cross-validation. Most of the 'pec' functionality has been moved to 'riskRegression'.

It provides functions to perform permutation conditional random one-sample and two-samples t-tests in a multivariate framework.

Data files and documentation for PEDiatric vALidation oF vAriableS
    in TBI (PEDALFAST).  The data was used in "Functional Status Scale in
    Children With Traumatic Brain Injury: A Prospective Cohort Study" by Bennett,
    Dixon, et al  (2016) <doi:10.1097/PCC.0000000000000934>.

Data and utilities for estimating pediatric blood pressure
    percentiles by sex, age, and optionally height (stature) as described in
    Martin et.al. (2022) <doi:10.1001/jamanetworkopen.2022.36918>.
    Blood pressure percentiles for children under one year of age come from Gemelli
    et.al. (1990) <doi:10.1007/BF02171556>.  Estimates of blood pressure
    percentiles for children at least one year of age are informed by
    data from the National Heart, Lung, and Blood Institute (NHLBI) and the
    Centers for Disease Control and Prevention (CDC)
    <doi:10.1542/peds.2009-2107C> or from Lo et.al. (2013)
    <doi:10.1542/peds.2012-1292>.  The flowchart for selecting the informing
    data source comes from Martin et.al. (2022)
    <doi:10.1542/hpeds.2021-005998>.