Likelihood-Based Intrinsic Dimension Estimators
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Documentation for package ‘intRinsic’ version 0.2.1
Help Pages
| autoplot.gride_bayes | Plot the simulated MCMC chains |
| autoplot.gride_evolution | Plot the evolution of 'Gride' estimates |
| autoplot.gride_mle | Plot the simulated bootstrap sample for the MLE 'Gride' |
| autoplot.Hidalgo | Plot the output of the 'Hidalgo' function |
| autoplot.twonn_bayes | Plot the output of the 'TWO-NN' model estimated via the Bayesian approach |
| autoplot.twonn_linfit | Plot the output of the 'TWO-NN' model estimated via least squares |
| autoplot.twonn_mle | Plot the output of the 'TWO-NN' model estimated via the Maximum Likelihood approach |
| auxHidalgo | Auxiliary functions for the 'Hidalgo' model |
| clustering | Posterior similarity matrix and partition estimation |
| compute_mus | Compute the ratio statistics needed for the intrinsic dimension estimation |
| credible_intervals | Auxiliary functions for the 'Hidalgo' model |
| dgera | The Generalized Ratio distribution |
| generalized_ratios_distribution | The Generalized Ratio distribution |
| gride | 'Gride': the Generalized Ratios ID Estimator |
| gride_evolution | 'Gride' evolution based on Maximum Likelihood Estimation |
| Hidalgo | Fit the 'Hidalgo' model |
| id_by_class | Stratification of the 'id' by an external categorical variable |
| initial_values | Auxiliary functions for the 'Hidalgo' model |
| plot.gride_bayes | 'Gride': the Generalized Ratios ID Estimator |
| plot.gride_evolution | 'Gride' evolution based on Maximum Likelihood Estimation |
| plot.gride_mle | 'Gride': the Generalized Ratios ID Estimator |
| plot.Hidalgo | Fit the 'Hidalgo' model |
| plot.hidalgo_psm | Posterior similarity matrix and partition estimation |
| plot.mus | Compute the ratio statistics needed for the intrinsic dimension estimation |
| plot.twonn_bayes | 'TWO-NN' estimator |
| plot.twonn_dec_by | Estimate the decimated 'TWO-NN' evolution with halving steps or vector of proportions |
| plot.twonn_dec_prop | Estimate the decimated 'TWO-NN' evolution with halving steps or vector of proportions |
| plot.twonn_linfit | 'TWO-NN' estimator |
| plot.twonn_mle | 'TWO-NN' estimator |
| posterior_means | Auxiliary functions for the 'Hidalgo' model |
| posterior_medians | Auxiliary functions for the 'Hidalgo' model |
| print.gride_bayes | 'Gride': the Generalized Ratios ID Estimator |
| print.gride_evolution | 'Gride' evolution based on Maximum Likelihood Estimation |
| print.gride_mle | 'Gride': the Generalized Ratios ID Estimator |
| print.Hidalgo | Fit the 'Hidalgo' model |
| print.hidalgo_class | Stratification of the 'id' by an external categorical variable |
| print.hidalgo_psm | Posterior similarity matrix and partition estimation |
| print.mus | Compute the ratio statistics needed for the intrinsic dimension estimation |
| print.summary.gride_bayes | 'Gride': the Generalized Ratios ID Estimator |
| print.summary.gride_mle | 'Gride': the Generalized Ratios ID Estimator |
| print.summary.Hidalgo | Fit the 'Hidalgo' model |
| print.summary.twonn_bayes | 'TWO-NN' estimator |
| print.summary.twonn_linfit | 'TWO-NN' estimator |
| print.summary.twonn_mle | 'TWO-NN' estimator |
| print.twonn_bayes | 'TWO-NN' estimator |
| print.twonn_dec_by | Estimate the decimated 'TWO-NN' evolution with halving steps or vector of proportions |
| print.twonn_dec_prop | Estimate the decimated 'TWO-NN' evolution with halving steps or vector of proportions |
| print.twonn_linfit | 'TWO-NN' estimator |
| print.twonn_mle | 'TWO-NN' estimator |
| rgera | The Generalized Ratio distribution |
| summary.gride_bayes | 'Gride': the Generalized Ratios ID Estimator |
| summary.gride_mle | 'Gride': the Generalized Ratios ID Estimator |
| summary.Hidalgo | Fit the 'Hidalgo' model |
| summary.twonn_bayes | 'TWO-NN' estimator |
| summary.twonn_linfit | 'TWO-NN' estimator |
| summary.twonn_mle | 'TWO-NN' estimator |
| Swissroll | Generates a noise-free Swiss roll dataset |
| twonn | 'TWO-NN' estimator |
| twonn_decimated | Estimate the decimated 'TWO-NN' evolution with halving steps or vector of proportions |