A Stochastic Block Model for Multilevel Networks
person(given = "Saint-Clair",
family = "Chabert-Liddell",
role = c("aut", "cre"),
email = "saint-clair.chabert-liddell@agroparistech.fr",
comment = c(ORCID = "0000-0001-5604-7308"))
Simulation, inference and clustering of multilevel networks using a
Stochastic Block Model framework as described in Chabert-Liddell, Barbillon,
Donnet and Lazega (2021) <doi:10.1016/j.csda.2021.107179>.
A multilevel network is defined as the junction of two interaction networks,
the upper level or inter-organizational level and the lower level or
inter-individual level. The inter-level represents an affiliation
relationship.
testthat (>= 2.1.0), covr, knitr, rmarkdown, ggplot2 (>=
3.3.2), tidygraph (>= 1.2.0), ggraph (>= 2.0.3), ggforce,
spelling
Saint-Clair Chabert-Liddell [aut, cre]
(<https://orcid.org/0000-0001-5604-7308>)

person(given = "Saint-Clair",
family = "Chabert-Liddell",
role = c("aut", "cre"),
email = "academic@chabert-liddell.com",
comment = c(ORCID = "0000-0001-5604-7308"))
testthat (>= 2.1.0), covr, knitr, rmarkdown, ggplot2 (>=
3.3.2), ggforce, spelling, cowplot, reshape2, dplyr

Mean Length-Based Estimators of Mortality using TMB
c(person("Quang", "Huynh", email = "q.huynh@oceans.ubc.ca", role = c("aut", "cre")),
person("Todd", "Gedamke", rol = "ctb"), person("Amy", "Then", rol = "ctb"))
Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) <doi:10.1577/T05-153.1>.
methods, stats, graphics, grDevices, dplyr (>= 0.5.0), gplots,
ggplot2 (>= 2.0.0), reshape2 (>= 1.4.1), parallel, TMB
Quang Huynh [aut, cre],
Todd Gedamke [ctb],
Amy Then [ctb]

c(person("Quang", "Huynh", email = "quang@bluematterscience.com", role = c("aut", "cre")),
person("Todd", "Gedamke", role = "ctb"),
person("Amy", "Then", role = "ctb"))

Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic curves (ROCs), precision-recall (PR) curves, and PR gain curves. 'MLeval' can automatically interpret the Caret train function results from repeated cross validation, select the best model, and produces a number of evaluation metrics with confidence intervals.