Description: A package for calculating and visualizing survey data statistics on a color-coded map.
Depends: R (>= 3.0.2), survey, lattice, maptools
Packaged: 2014-04-14 19:29:18 UTC; Sam Ackerman

Title: Geographic Display of Survey Data Statistics
Description: Automated calculation and visualization of survey data statistics on a color-coded map.
Imports: RColorBrewer, Hmisc, classInt, sp, colorspace, reshape2
Packaged: 2015-02-19 12:16:40 UTC; Sam Ackerman

Packaged: 2015-04-22 15:17:06 UTC; Sam Ackerman

Title: Unbiased Thematic Map Accuracy and Area
Imports: reshape, stats, assertthat, gtools
Author: Hugo Costa [cre, aut] <hugoagcosta@gmail.com>
Description: Unbiased estimators of overall and per-class thematic map accuracy and area published in Olofsson et al. (2014) <doi:10.1016/j.rse.2014.02.015> and Stehman (2014) <doi:10.1080/01431161.2014.930207>.

Author: Hugo Costa [aut, cre] (<https://orcid.org/0000-0001-6207-8223>)

Title: MAP-Bayesian Estimation of PK Parameters
Description: Performs maximum a posteriori Bayesian estimation of individual pharmacokinetic parameters from a model defined in `mrgsolve`, typically for model-based therapeutic drug monitoring. Internally computes an objective function value from model and data, performs optimization and returns predictions in a convenient format.
Imports: dplyr, ggplot2, magrittr, mrgsolve (>= 0.10.9), optimx, purrr,
Maintainer: Felicien Le Louedec <lelouedec.felicien@iuct-oncopole.fr>

c(person(given = "Felicien", family = "Le Louedec",
person(given = "Kyle T", family = "Baron",
Imports: dplyr, ggplot2, magrittr, mrgsolve (>= 1.0.0), optimx, purrr,

Imports: dplyr, ggplot2, magrittr, mrgsolve (>= 1.0.0), optimx,
progress, purrr, rlang, stringr, tibble, tidyr
Suggests: knitr, minqa, testthat (>= 3.0.0)

Description: Performs maximum a posteriori Bayesian estimation of individual pharmacokinetic parameters from a model defined in 'mrgsolve', typically for model-based therapeutic drug monitoring. Internally computes an objective function value from model and data, performs optimization and returns predictions in a convenient format. The performance of the package was described by Le Louedec et al (2021) <doi:10.1002/psp4.12689>.
Maintainer: Felicien Le Louedec <felicienlelouedec@proton.me>