Title: Basic Sensitivity Analysis of Epidemiological Results
Authors@R: person("Denis", "Haine", email = "denis.haine@gmail.com",
Description: Basic sensitivity analysis of the observed relative risks adjusting
for unmeasured confounding and misclassification of the
exposure/outcome, or both. It follows the bias analysis methods and
examples from the book by Lash T.L, Fox M.P, and Fink A.K. Applying
Quantitative Bias Analysis to Epidemiologic Data, Springer, 2009.

Imports: triangle, trapezoid, plyr, ggplot2, grid, gridExtra
examples from the book by Lash T.L, Fox M.P, and Fink A.K.
"Applying Quantitative Bias Analysis to Epidemiologic Data",

Imports: triangle, trapezoid, plyr, ggplot2, grid, gridExtra, reshape,
Suggests: testthat, knitr, rmarkdown, aplore3

Imports: triangle, trapezoid, actuar, llogistic, logitnorm, plyr,

Imports: triangle, trapezoid, actuar, plyr, ggplot2, grid, gridExtra,

Suggests: testthat, knitr, rmarkdown, aplore3, covr

Imports: triangle, trapezoid, actuar, dagitty, ggdag, boot, magrittr
Suggests: testthat, knitr, rmarkdown, aplore3, directlabels, tidyverse,

person("James", "Black", , "james@epijim.uk", c("aut", "cre")),
Description: A collection of R functions supporting the text book
Modern Epidemiology, Second Edition, by Kenneth J.Rothman and Sander Greenland.
ISBN 13: 978-0781755641  See <http://www.krothman.org/> for more information.
Imports: dplyr, tidyr, ggplot2, magrittr

person("Simon", "Thelwall", , "simon.thelwall@gmail.com", c("ctb"),
Imports: dplyr, tidyr, ggplot2, magrittr, assertthat, rlang

Title: Estimation with Penalisation in Systems of Ordinary Differential
Authors@R: person("Frederik Vissing", "Mikkelsen", email = "mifretz@gmail.com", role = c("aut", "cre"))
Maintainer: Frederik Vissing Mikkelsen <mifretz@gmail.com>
Description: A set of statistical tools for inferring unknown parameters in continuous time processes governed by ordinary differential equations (ODE). Moreover, variance reduction and model selection can be obtained through various implemented penalisation schemes. The package offers two estimation procedures: exact estimation via least squares and a faster approximate estimation via inverse collocation methods. All estimators can handle multiple data sets arising from the same ODE system, but subjected to different interventions.
Author: Frederik Vissing Mikkelsen [aut, cre]