Package: rubias 0.3.4

rubias: Bayesian Inference from the Conditional Genetic Stock Identification Model

Implements Bayesian inference for the conditional genetic stock identification model. It allows inference of mixed fisheries and also simulation of mixtures to predict accuracy. A full description of the underlying methods is available in a recently published article in the Canadian Journal of Fisheries and Aquatic Sciences: <doi:10.1139/cjfas-2018-0016>.

Authors:Eric C. Anderson [aut, cre], Ben Moran [aut]

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NEWS

# Install 'rubias' in R:
install.packages('rubias', repos = c('https://eriqande.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/eriqande/rubias/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • alewife - Microsat data from alewife herring reference populations
  • blueback - Microsat data from blueback herring reference populations
  • chinook - SNP data from chinook reference populations
  • chinook_collection_levels - A vector that gives a desired sort order of the chinook collections
  • chinook_mix - SNP data from Chinook salmon taken in May/August 2015 from California fisheries
  • chinook_repunit_levels - A vector that gives a desired sort order of the chinook repunits
  • perfect_chinook - Perfect-assignment genetic data for chinook.
  • perfect_chinook_mix - Perfect-assignment mixture genetic data for chinook.
  • sim_spec_examples - List of example ways of specifying repunit and collection quantities in simulations
  • small_chinook_mix - Small sample of SNP data from Chinook salmon taken in May/August 2015 from California fisheries
  • small_chinook_ref - SNP data from selected chinook reference populations

On CRAN:

noaa-omics-software

5.73 score 2 stars 90 scripts 221 downloads 2 mentions 46 exports 34 dependencies

Last updated 10 months agofrom:bbb1753cfa. Checks:OK: 3 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-win-x86_64OKOct 31 2024
R-4.5-linux-x86_64OKOct 31 2024
R-4.4-win-x86_64NOTEOct 31 2024
R-4.4-mac-x86_64NOTEOct 31 2024
R-4.4-mac-aarch64NOTEOct 31 2024
R-4.3-win-x86_64NOTEOct 31 2024
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Exports:%>%a_freq_listallelic_listassess_pb_bias_correctionassess_reference_looassess_reference_mcavg_coll2correctRUbootstrap_rhocheck_known_collectionscheck_refmixclose_matching_samplescount_missing_datacustom_pi_priordirch_from_allocationsdirch_from_countsgeno_logLgeno_logL_ssqgprob_sim_gcgprob_sim_gc_missinggprob_sim_indgsi_em_1gsi_mcmc_1gsi_mcmc_fbHasselman_sim_collsinfer_mixturelist_diploid_paramsmixture_drawmodify_scaled_likelihoods_for_known_mixture_fishper_locus_means_and_varsrcpp_close_matchersrcpp_indiv_specific_logl_means_and_varsrcpp_per_locus_loglsread_gsi_simref_and_mix_pipelinereference_allele_countsround2samp_from_matself_assignsimulate_random_samplestcf2longtcf2param_listtidy_mcmc_coll_rep_stufftidy_mcmc_pofztidy_pi_traceswrite_gsi_sim_mixturewrite_gsi_sim_reference

Dependencies:bitbit64clicliprcpp11crayondplyrfansigenericsgluegtoolshmslifecyclemagrittrpillarpkgconfigprettyunitsprogresspurrrR6RcppRcppParallelreadrrlangstringistringrtibbletidyrtidyselecttzdbutf8vctrsvroomwithr

An Explanation of the Underlying Data Structures in rubias

Eric C. Anderson

Rendered fromrubias-underlying-data-structures.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2018-03-30
Started: 2018-01-24

An Overview of rubias Usage

Eric C. Anderson

Rendered fromrubias-overview.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2021-01-15
Started: 2018-01-24

Using the Fully Bayesian Model in rubias

Ben Moran

Rendered fromrubias-fully-bayesian.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2019-06-10
Started: 2019-06-09

Readme and manuals

Help Manual

Help pageTopics
Microsat data from alewife herring reference populationsalewife
Test the effects of the parametric bootstrap bias correction on a reference dataset through cross-validationassess_pb_bias_correction
Simulate mixtures and estimate reporting group and collection proportions.assess_reference_loo
Partition a reference dataset and estimate reporting group and collection proportionsassess_reference_mc
Microsat data from blueback herring reference populationsblueback
SNP data from chinook reference populationschinook
a vector that gives a desired sort order of the chinook collectionschinook_collection_levels
SNP data from Chinook salmon taken in May/August 2015 from California fisherieschinook_mix
a vector that gives a desired sort order of the chinook repunitschinook_repunit_levels
check for matching (or close to matching) genotypes in a data frameclose_matching_samples
Estimate mixing proportions and origin probabilities from one or several mixturesinfer_mixture
perfect-assignment genetic data for chinook.perfect_chinook
perfect-assignment mixture genetic data for chinook.perfect_chinook_mix
read a gsi_sim formatted input file into a tibble that rubias can useread_gsi_sim
rubias: Bayesian inference from the conditional genetic stock identification modelrubias
Do leave-one-out self-assignment of individuals in a reference baselineself_assign
List of example ways of specifying repunit and collection quantities in simulationssim_spec_examples
Small sample of SNP data from Chinook salmon taken in May/August 2015 from California fisheriessmall_chinook_mix
SNP data from selected chinook reference populationssmall_chinook_ref
Write a mixture data frame to gsi_sim format baseline and repunits filewrite_gsi_sim_mixture
Write a reference data frame to gsi_sim format baseline and repunits filewrite_gsi_sim_reference