rubias: rubias: Bayesian inference from the conditional genetic stock...
In benmoran11/rubias: Bayesian Inference from the Conditional Genetic Stock Identification Model
rubias R Documentation
rubias: Bayesian inference from the conditional genetic stock identification model
Description
Read the "rubias-overview" vignette for information on data input formats
and how to use the package
the rubias main high-level functions
The following functions are wrappers, designed for user-friendly input and useful output:
infer_mixture is used to perform genetic stock identification.
Options include standard MCMC and the parametric bootstrap bias correction.
self_assign does simple self-assignment of individuals in a reference data set
to the various collections in the reference data set.
assess_reference_loo does leave-one-out based simulations to predict how
accurately GSI can be done.
assess_reference_mc uses Monte-Carlo cross-validation based simulations
to predict how accurately GSI can be done.
assess_pb_bias_correction attempts to demonstrate how much (or little)
improvement can be expected from the parametric bootstrap correction given a particular
reference data set.
genetic data format
See the vignette.
example data
alewife, blueback, and chinook are
genetic data sets that are useful for playing around with rubias and testing it
out.
Author(s)
Maintainer: Eric C. Anderson eric.anderson@noaa.gov
Authors:
Ben Moran moran.ben@husky.neu.edu
benmoran11/rubias documentation built on June 10, 2025, 11:27 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| rubias | R Documentation |
rubias: Bayesian inference from the conditional genetic stock identification model
Description
Read the "rubias-overview" vignette for information on data input formats and how to use the package
the rubias main high-level functions
The following functions are wrappers, designed for user-friendly input and useful output:
infer_mixture is used to perform genetic stock identification.
Options include standard MCMC and the parametric bootstrap bias correction.
self_assign does simple self-assignment of individuals in a reference data set
to the various collections in the reference data set.
assess_reference_loo does leave-one-out based simulations to predict how
accurately GSI can be done.
assess_reference_mc uses Monte-Carlo cross-validation based simulations
to predict how accurately GSI can be done.
assess_pb_bias_correction attempts to demonstrate how much (or little)
improvement can be expected from the parametric bootstrap correction given a particular
reference data set.
genetic data format
See the vignette.
example data
alewife, blueback, and chinook are
genetic data sets that are useful for playing around with rubias and testing it
out.
Author(s)
Maintainer: Eric C. Anderson eric.anderson@noaa.gov
Authors:
Ben Moran moran.ben@husky.neu.edu
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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