Package: intRinsic 1.1.0
Francesco Denti
intRinsic: Likelihood-Based Intrinsic Dimension Estimators
Provides functions to estimate the intrinsic dimension of a dataset via likelihood-based approaches. Specifically, the package implements the 'TWO-NN' and 'Gride' estimators and the 'Hidalgo' Bayesian mixture model. In addition, the first reference contains an extended vignette on the usage of the 'TWO-NN' and 'Hidalgo' models. References: Denti (2023, <doi:10.18637/jss.v106.i09>); Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>); Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).
Authors:
intRinsic_1.1.0.tar.gz
intRinsic_1.1.0.tar.gz(r-4.5-noble)intRinsic_1.1.0.tar.gz(r-4.4-noble)
intRinsic_1.1.0.tgz(r-4.4-emscripten)intRinsic_1.1.0.tgz(r-4.3-emscripten)
intRinsic.pdf |intRinsic.html✨
intRinsic/json (API)
NEWS
| # Install 'intRinsic' in R: |
| install.packages('intRinsic', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/fradenti/intrinsic/issues
Last updated 3 months agofrom:d273595553. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 12 2024 |
| R-4.5-linux-x86_64 | OK | Nov 12 2024 |
Exports:autoplotclusteringcompute_muscredible_intervalsdgeragridegride_evolutionHidalgoid_by_classinitial_valuesposterior_meansposterior_mediansrgeraSwissrolltwonntwonn_decimatedtwonn_decimation
Dependencies:clicolorspacedplyrevaluatefansifarverFNNgenericsggplot2gluegtablehighrisobandknitrlabelinglatex2explatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RColorBrewerRcppRcppArmadilloreshape2rlangsalsoscalesstringistringrtibbletidyselectutf8vctrsviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Plot the simulated MCMC chains for the Bayesian 'Gride' | autoplot.gride_bayes |
| Plot the evolution of 'Gride' estimates | autoplot.gride_evolution |
| Plot the simulated bootstrap sample for the MLE 'Gride' | autoplot.gride_mle |
| Plot the output of the 'Hidalgo' function | autoplot.Hidalgo |
| Plot the output of the 'TWO-NN' model estimated via the Bayesian approach | autoplot.twonn_bayes |
| Plot the output of the 'TWO-NN' model estimated via least squares | autoplot.twonn_linfit |
| Plot the output of the 'TWO-NN' model estimated via the Maximum Likelihood approach | autoplot.twonn_mle |
| Auxiliary functions for the 'Hidalgo' model | auxHidalgo credible_intervals initial_values posterior_means posterior_medians |
| Posterior similarity matrix and partition estimation | clustering plot.hidalgo_psm print.hidalgo_psm |
| Compute the ratio statistics needed for the intrinsic dimension estimation | compute_mus plot.mus print.mus print.mus_Nq |
| The Generalized Ratio distribution | dgera generalized_ratios_distribution rgera |
| 'Gride': the Generalized Ratios ID Estimator | gride plot.gride_bayes plot.gride_mle print.gride_bayes print.gride_mle print.summary.gride_bayes print.summary.gride_mle summary.gride_bayes summary.gride_mle |
| 'Gride' evolution based on Maximum Likelihood Estimation | gride_evolution plot.gride_evolution print.gride_evolution |
| Fit the 'Hidalgo' model | Hidalgo plot.Hidalgo print.Hidalgo print.summary.Hidalgo summary.Hidalgo |
| Stratification of the 'id' by an external categorical variable | id_by_class print.hidalgo_class |
| Generates a noise-free Swiss roll dataset | Swissroll |
| 'TWO-NN' estimator | plot.twonn_bayes plot.twonn_linfit plot.twonn_mle print.summary.twonn_bayes print.summary.twonn_linfit print.summary.twonn_mle print.twonn_bayes print.twonn_linfit print.twonn_mle summary.twonn_bayes summary.twonn_linfit summary.twonn_mle twonn |
| Estimate the decimated 'TWO-NN' evolution with halving steps or vector of proportions | twonn_decimated |
| Estimate the decimated 'TWO-NN' evolution with halving steps or vector of proportions | plot.twonn_dec_by plot.twonn_dec_prop print.twonn_dec_by print.twonn_dec_prop twonn_decimation |
