(McLure et al. Environmental Modelling and Software.
<DOI:10.1016/j.envsoft.2021.105158>). The package is currently in early stages,
however more features are planned or in the works: e.g. adjustments for
imperfect test specificity/sensitivity, functions for helping with optimal
experimental design, and functions for spatial modelling.

Title: Predict 10-Year Risk for Atherosclerotic Cardiovascular Disease
Description: The 2017 American College of Cardiology and American Heart
Association blood pressure guideline recommends using 10-year predicted
atherosclerotic cardiovascular disease risk to guide the decision to
initiate or intensify antihypertensive medication. The guideline recommends
using the Pooled Cohort risk prediction equations to predict 10-year
atherosclerotic cardiovascular disease risk. This package implements the
original Pooled Cohort risk prediction equations and also incorporates
updated versions based on more contemporary data and statistical methods.
Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R,
Greenland P, Lackland DT, Levy D, O’Donnell CJ, and Robinson JG (2014)
Yadlowsky S, Hayward RA, Sussman JB, McClelland RL, Min YI, and Basu S (2018)
Author: Byron Jaeger [aut, cre] (<https://orcid.org/0000-0001-7399-2299>)

Title: Pooled Mean Group Estimation of Dynamic Heterogenous Panels
Authors@R: c(person("Piotr", "Zientara", role="aut", email="zientara@fest.pl"),
person("Lech", "Kujawski", role=c("aut", "cre"), email = "lech.kujawski@ug.edu.pl"))
Description: Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>.

Title: PopED: Population (and individual) optimal Experimental Design
Author: Andrew C. Hooker <andrew.hooker@farmbio.uu.se>
Maintainer: Andrew C. Hooker <andrew.hooker@farmbio.uu.se>
Description: PopED computes optimal experimental designs for both
population studies and individual studies based on nonlinear mixed-effect models.
Often this is based on a computation of the Fisher Information Matrix (FIM).