Title: Penalized Isotonic Regression in one and two dimensions
Author: Mary C Meyer, Jiwen Wu, and Jean D. Opsomer
Description: Given a response y and a one- or two-dimensional predictor, the isotonic regression estimator is calculated with the usual orderings.
Depends: graphics, grDevices, stats, utils, coneproj, Matrix
Packaged: 2014-04-04 22:31:54 UTC; marycmeyer

Title: Isotopic Tracer Analysis Using MCMC
Authors@R: c(person("Andrés", "López-Sepulcre", email = "lopezsepulcre@gmail.com",
person("Matthieu", "Bruneaux", email = "matthieu.bruneaux@gmail.com",
Description: 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)
Imports: coda (>= 0.19-3), data.table, dplyr (>= 0.8.5), latex2exp (>=
Suggests: bayesplot, covr, ggdist, ggplot2, ggraph, gridBase,
gridExtra, here, igraph, knitr, lattice, readxl, rmarkdown,
Author: Andrés López-Sepulcre [aut] (<https://orcid.org/0000-0001-9708-0788>),
Matthieu Bruneaux [aut, cre] (<https://orcid.org/0000-0001-6997-192X>)

Description: 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>),
and fair-cut forest (Cortes (2019) <arXiv:1911.06646>),
for isolation-based outlier detection, clustered outlier detection, distance or similarity
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.

Suggests: MASS, outliertree, jsonlite, readr

comment="Copyright holder of original xoshiro code"),
fair-cut forest (Cortes (2021) <arXiv:2110:13402>),
robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) <http://proceedings.mlr.press/v48/guha16.html>),
and customizable variations of them, for isolation-based outlier detection, clustered outlier detection,
distance or similarity approximation (Cortes (2019) <arXiv:1910.12362>),
isolation kernel calculation (Ting, Zhu, Zhou (2018) <doi:10.1145/3219819.3219990>),
based on random or guided decision tree splitting, and providing different metrics for
scoring anomalies based on isolation depth or density (Cortes (2021) <arXiv:2111.11639>).
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.
Suggests: MASS, outliertree, jsonlite (>= 1.7.3), mlbench, MLmetrics,
David Blackman [cph] (Copyright holder of original xoshiro code),
Sebastiano Vigna [cph] (Copyright holder of original xoshiro code),