Plot Positive and Negative Predictive Values for Medical Tests
c(person("Gorka", "Navarrete", email = "gorkang@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7678-8656")))
Functions to plot and help understand positive and negative predictive values (PPV and NPV), and their relationship with sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007) <doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of the technical concepts and Navarrete et al. (2015) for a practical explanation about the importance of their understanding <doi:10.3389/fpsyg.2015.01327>.
dplyr, reshape2, ggplot2, tidyr, magrittr, tibble, ggforce,
shinythemes, shinyjs
Gorka Navarrete [aut, cre] (<https://orcid.org/0000-0001-7678-8656>)

dplyr, reshape2, ggplot2, tidyr, magrittr, tibble, ggforce
testthat, knitr, rmarkdown, covr, patchwork

dplyr, reshape2, ggplot2, tidyr, magrittr, tibble, ggforce,
stats
glue, purrr, testthat, knitr, rmarkdown, covr, patchwork

person("Gorka", "Navarrete", , "gorkang@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-7678-8656"))
Functions to plot and help understand positive and negative
predictive values (PPV and NPV), and their relationship with
sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007)
<doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of
the technical concepts and Navarrete et al. (2015) for a practical
explanation about the importance of their understanding
<doi:10.3389/fpsyg.2015.01327>.
dplyr, ggforce, ggplot2, magrittr, reshape2, stats, tibble,
tidyr, utils
curl, httr, knitr, patchwork, purrr, rmarkdown, testthat

General-Purpose MCMC and SMC Samplers and Tools for Bayesian
Statistics
c(person("Florian", "Hartig", email = "florian.hartig@biologie.uni-regensburg.de", role = c("aut", "cre")), person("Francesco", "Minunno", role = c("aut")), person("Stefan", " Paul", role = c("aut") ),  person("David", "Cameron", role = "ctb"))
Florian Hartig [aut, cre],
Francesco Minunno [aut],
Stefan Paul [aut],
David Cameron [ctb]
General-purpose MCMC and SMC samplers, as well as plot and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.
coda, vioplot, emulator, mvtnorm, IDPmisc, Rcpp, ellipse,
numDeriv, msm, MASS, Matrix, stats, utils, graphics
DEoptim, lhs, sensitivity, knitr, rmarkdown, roxygen2,
testthat, gap