Package: RHISEA 0.1.0

RHISEA: An R package for flexible and reproducible mixed-stock analysis in fisheries and ecology

RHISEA modernizes Millar's HISEA (1987,1990) methodology for reproducible mixed-stock analysis. RHISEA provides a flexible and reproducible framework for mixed-stock analysis in fisheries and ecology. Modernizes legacy workflows by integrating modern statistical classifiers (LDA, RF, MCLUST, SVM, etc.) and estimators (EM, ML, COOK). Includes simulation frameworks, bootstrap resampling, and complete analysis workflows for fisheries stock assessment and population structure inference.

Authors:Sosthene A. V. Akia [aut, cre, cph], Alex Hanke [aut, cph]

RHISEA_0.1.0.tar.gz
RHISEA_0.1.0.zip(r-4.7)RHISEA_0.1.0.zip(r-4.6)RHISEA_0.1.0.zip(r-4.5)
RHISEA_0.1.0.tgz(r-4.6-any)RHISEA_0.1.0.tgz(r-4.5-any)
RHISEA_0.1.0.tar.gz(r-4.7-any)RHISEA_0.1.0.tar.gz(r-4.6-any)
RHISEA_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
RHISEA/json (API)

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

Bug tracker:https://github.com/sosthenea/rhisea/issues

Pkgdown/docs site:https://sosthenea.github.io

Datasets:
  • baseline - Baseline Data for RHISEA
  • mixture - Mixture dataset for RHISEA examples

On CRAN:

Conda:

4.24 score 20 exports 79 dependencies

Last updated from:7752da424d. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE186
source / vignettesOK273
linux-release-x86_64NOTE188
macos-release-arm64NOTE189
macos-oldrel-arm64NOTE225
windows-develNOTE119
windows-releaseNOTE127
windows-oldrelNOTE134
wasm-releaseOK133

Exports:accel_em_algorithmclassify_samplescompute_cook_estimatorscompute_ldf_coefficientscreate_hisea_summary_reportem_algorithmestimate_millar_theta4estimate_ml_theta5get_phiordvecplot_hisea_thetaprint_hisea_summaryprocess_hisea_inputread_baselineread_mixturerun_hisea_allrun_hisea_estimatessimulate_mixturewrite_mix_from_dataframewrite_std_from_dataframe

Dependencies:caretclasscliclockcodetoolscpp11data.tablediagramdigestdplyre1071farverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatigraphipredisobanditeratorsjsonliteKernSmoothkknnlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmclustModelMetricsnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6randomForestRColorBrewerRcpprecipesreshape2rlangrpartS7scalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrxgboost

Comparing built-in classification methods in RHISEA for mixed-stock analysis
Introduction | Setup and Required Libraries | Load Baseline and Mixture Data | Define Analysis Parameters and Output Setup | Supported Classifiers to Evaluate | Run Automated Classifier Comparison | Summarize and Visualize Classifier Performance | Plot Accuracy and Kappa for Each Model | Conclusion | Appendix: Additional Visualizations and Statistics
Rendered fromMultiple_Classifiers.Rmdusingknitr::rmarkdown

Last update: 2026-03-17
Started: 2025-08-13

Two input paths in RHISEA: run_hisea_all with HISEA-style files or Dataframes
Introduction | Required packages | Load the data | Write HISEA formatted files (optional) | Analysis parameters | 1) Run with Data frame inputs | 2) Run with HISEA-formatted files | Compare results: table + plot | Visualize bootstrap distributions (if available) | Interpretation & Reasoning (how to read results)
Rendered fromRHISEA_data_type.Rmdusingknitr::rmarkdown

Last update: 2026-03-17
Started: 2025-08-19

Analyzing complex mixed-stock fisheries with 20 stocks
Introduction | Data Simulation of Classification Outputs | Running Classic HISEA Estimators with run_hisea_estimates() | Displaying and Plotting Results | Conclusion
Rendered fromcomplex_mixed_stocks.Rmdusingknitr::rmarkdown

Last update: 2026-03-17
Started: 2025-10-31

Applying HISEA estimators to custom classification models with run_hisea_estimates
Introduction | Purpose of run_hisea_estimates() | What you need to provide | How it works internally | 1. Linear Discriminant Analysis (LDA) | 2. Random Forest (RF) | 3. Conditional Inference Tree (CTREE) | Conclusion
Rendered fromCustom_Classification_Models.Rmdusingknitr::rmarkdown

Last update: 2026-03-17
Started: 2025-08-13

Genetic assignment to Mixed-Stock Analysis with RHISEA
Overview | Data preparation and import | Assignment cross-validation | Assignment of unknown individuals | From assignment matrices to RHISEA inputs | Mixed-stock estimation with RHISEA
Rendered fromEstimate_genetics_data.Rmdusingknitr::rmarkdown

Last update: 2026-03-17
Started: 2025-12-09

Comprehensive Guide to the RHISEA Package for Mixed-Stock Analysis
1) Data Preparation | 2) Using run_hisea_all() with Built-in Models | 3) Testing Parameters: phi_method and type in RF Model | 4) Using run_hisea_estimates() with External Models and Comparison | 5) Tuning Built-in Random Forest Models with run_hisea_all() | 6) Tuning External Random Forest Models with run_hisea_estimates() and Comparison
Rendered fromRHISEA_comprehensive_Guide.Rmdusingknitr::rmarkdown

Last update: 2026-03-17
Started: 2025-10-31

Verifying compatibility: RHISEA (R) vs FORTRAN HISEA
Introduction | 1. Load baseline and mixture data from installed package | 2. Define global parameters for simulation and analysis | 3. Utility functions for reading/writing HISEA files and Fortran outputs | 4. Simulation setup and results containers | 5. Main simulation loop | 6. Aggregate and summarize results | 7. Export results to Excel | 8. Visualization: Compare R vs Fortran estimators | 9. Visualization: Compare R_MASS vs Fortran estimators | 10. Boxplot for all estimators and methods | 11. Diagnostics: Alert if large divergence detected | Conclusion
Rendered fromRHISEA_vs_Fortran.Rmdusingknitr::rmarkdown

Last update: 2026-03-17
Started: 2025-08-19