c(person("Andrés", "López-Sepulcre", email = "lopezsepulcre@gmail.com",
role = c("aut"),
comment = c(ORCID = "0000-0001-9708-0788")),
person("Matthieu", "Bruneaux", email = "matthieu.bruneaux@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-6997-192X")))
Implements Bayesian models to analyze data from tracer addition
experiments. The implemented method was originally described in the article
"A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from
Isotope Tracer Addition Experiments" by López-Sepulcre et al. (2020)
<doi:10.1086/708546>.
bayesplot, covr, ggdist, ggplot2, ggraph, gridBase,
gridExtra, here, igraph, knitr, lattice, readxl, rmarkdown,
testthat, tidygraph, tidyverse, viridisLite
Andrés López-Sepulcre [aut] (<https://orcid.org/0000-0001-9708-0788>),
Matthieu Bruneaux [aut, cre] (<https://orcid.org/0000-0001-6997-192X>)

Fast and multi-threaded implementation of
isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>),
extended isolation forest (Hariri, Kind, Brunner (2018) <arXiv:1811.02141>),
SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>),
and fair-cut forest (Cortes (2019) <arXiv:1911.06646>),
for isolation-based outlier detection, clustered outlier detection, distance or similarity
approximation (Cortes (2019) <arXiv:1910.12362>),
and imputation of missing values (Cortes (2019) <arXiv:1911.06646>),
based on random or guided decision tree splitting. Provides simple heuristics for fitting the model to
categorical columns and handling missing data, and offers options for varying between random and guided
splits, and for using different splitting criteria.

Fast and multi-threaded implementation of
isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>),
extended isolation forest (Hariri, Kind, Brunner (2018) <arXiv:1811.02141>),
SCiForest (Liu, Ting, Zhou, (2010) <doi:10.1007/978-3-642-15883-4_18>),
and fair-cut forest (Cortes (2019) <arXiv:1911.06646>),
for isolation-based outlier detection, clustered outlier detection, distance or similarity
approximation (Cortes (2019) <arXiv:1910.12362>),
and imputation of missing values (Cortes (2019) <arXiv:1911.06646>),
based on random or guided decision tree splitting. Provides simple heuristics for fitting the model to
categorical columns and handling missing data, and offers options for varying between random and guided
splits, and for using different splitting criteria.