Author: Sara Perez-Jaume, Natalia Pallares, Konstantina Skaltsa
Description: Point and interval estimations of optimum thresholds for continuous diagnostic tests (two- and three- state settings).
Depends: R (>= 3.1.0), MASS, numDeriv, pROC

Description: Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. The package also provides functions for sample size determination and estimation of diagnostic accuracy measures. It also includes graphical tools.

Description: Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. We also provide functions for sample size determination and estimation of diagnostic accuracy measures. We also include graphical tools. The statistical methodology used here can be found in Perez-Jaume et al (2017) <doi:10.18637/jss.v082.i04>.

Title: Threshold and AUC Estimation with Right-Censored Data at a Fixed
Authors@R: c(person("Sara", "Perez-Jaume", email="spjaume@gmail.com", role=c("aut", "cre")),
Description: We focus on the estimation of optimal thresholds and AUCs when the outcome of interest is the status (alive or dead) of the subjects at a certain time-point t. This binary status is determined by right-censored times to event and it is missing for those subjects censored before t. Here we provide three methods (missing exclusion, imputation of censored times and using time-dependent ROC curves) to estimate optimal thresholds and AUCs in this context. Two references for the methods used here are Skaltsa et al. (2010) <doi:10.1002/bimj.200900294> and Heagerty et al. (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.
Imports: boot, InformativeCensoring, pROC, psych, survival,

Title: Trajectories and Phylogenies Simulator
Authors@R: c(person("Gonche", "Danesh", role = c("aut", "cre", "cph"), email = "gonche.danesh@gmail.com"),
person("Emma", "Saulnier", role = c("aut", "cph")),
person("Marc", "Choisy", role = c("aut", "cph", "ths")),
person("Samuel", "Alizon", role = c("aut", "cph", "ths")))
Description: Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2020) <doi:10.1101/2020.11.09.373795>.
Imports: graphics, grDevices, gtools, inline, methods, Rcpp (>=