Package: intRinsic 1.1.0

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:Francesco Denti [aut, cre, cph], Andrea Gilardi [aut]

intRinsic_1.1.0.tar.gz
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intRinsic.pdf |intRinsic.html✨
intRinsic/json (API)
NEWS

# Install 'intRinsic' in R:

install.packages('intRinsic', repos = c('https://fradenti.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/fradenti/intrinsic/issues

Uses libs:

c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

On CRAN:

17 exports 11 stars 1.71 score 45 dependencies 10 scripts 294 downloads

Last updated 5 days agofrom:07f8fe7e84. Checks:OK: 9. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 13 2024
R-4.5-win-x86_64	OK	Sep 13 2024
R-4.5-linux-x86_64	OK	Sep 13 2024
R-4.4-win-x86_64	OK	Sep 13 2024
R-4.4-mac-x86_64	OK	Sep 13 2024
R-4.4-mac-aarch64	OK	Sep 13 2024
R-4.3-win-x86_64	OK	Sep 13 2024
R-4.3-mac-x86_64	OK	Sep 13 2024
R-4.3-mac-aarch64	OK	Sep 13 2024

Exports:autoplot clustering compute_mus credible_intervals dgera gride gride_evolution Hidalgo id_by_class initial_values posterior_means posterior_medians rgera Swissroll twonn twonn_decimated twonn_decimation

Dependencies:cli colorspace dplyr evaluate fansi farver FNN generics ggplot2 glue gtable highr isoband knitr labeling latex2exp lattice lifecycle magrittr MASS Matrix mgcv munsell nlme pillar pkgconfig plyr R6 RColorBrewer Rcpp RcppArmadillo reshape2 rlang salso scales stringi stringr tibble tidyselect utf8 vctrs viridisLite withr xfun yaml

Citation

Development and contributors

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