This package combines several sighting based estimators of historical extinction, allowing them to be run simultaneously or individually. Code for this package was contributed by Ben Collen, Gene Hunt and Tracy Rout. Additional code was taken from McPherson & Myers (2009).

Sequential Full Factorial-Based Latin Hypercube Design
Gives design points from a sequential full factorial-based
Latin hypercube design, as described in Duan, Ankenman, Sanchez,
and Sanchez (2015, Technometrics,
<doi:10.1080/00401706.2015.1108233>).

Survival Extension of the Generalized Berk-Jones Test
c(person(given = "Laura",
family = "Villain",
role = c("aut", "cre"),
email = "villain.laura@gmail.com"),
person(given = "Thomas",
family = "Ferte",
role = "aut"),
person(given = "Rodolphe",
family = "Thiebault",
role = "aut"),
person(given = "Boris P.",
family = "Hejblum",
role = "aut"))
Implements an extension of the Generalized Berk-Jones (GBJ) statistic for
survival data, sGBJ. It computes the sGBJ statistic and its p-value for testing
the association between a gene set and a time-to-event outcome with possible
adjustment on additional covariates. Detailed method is available at Villain L, Ferte T,
Thiebaut R and Hejblum BP (2021) <doi:10.1101/2021.09.07.459329>.
Laura Villain [aut, cre],
Thomas Ferte [aut],
Rodolphe Thiebault [aut],
Boris P. Hejblum [aut]

Sparse Gaussian Markov Random Field Mixtures for Anomaly
Detection
An implementation of sparse Gaussian Markov random field mixtures
presented by Ide et al. (2016) <doi:10.1109/ICDM.2016.0119>.
It provides a novel anomaly detection method for multivariate noisy sensor data.
It can automatically handle multiple operational modes.
And it can also compute variable-wise anomaly scores.
ggplot2, glasso, mvtnorm, stats, tidyr, utils, zoo
dplyr, ModelMetrics, testthat, covr, knitr, rmarkdown

Sparse Generalized Principal Component Analysis
Functions for computing sparse generalized principal components, including functions for modeling structured correlation

Assessment of Diagnostic and Prognostic Markers
c(person(given="Susana", family="Diaz-Coto", role = "aut", email = "susanacoto@gmail.com"), person(given="Susana", family="Diaz-Coto", role = "cre", email = "susanacoto@gmail.com"))
Provides estimations of the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) based on the two-stages mixed-subjects ROC curve estimator (Diaz-Coto et al. (2020) <doi:10.1515/ijb-2019-0097> and Diaz-Coto et al. (2020) <doi:10.1080/00949655.2020.1736071>).
survival, rms, foreach, ggplot2, flextable, plotROC, icenReg,
RISCA, thregI
Susana Diaz-Coto [aut],
Susana Diaz-Coto [cre]