| 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 |
| compute_mus | Compute the ratio statistics needed for the intrinsic dimension estimation |
| 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 | Gibbs sampler for the 'Hidalgo' model |
| id_by_class | Stratification of the 'id' by an external categorical variable |
| print.gride_bayes | Print 'Gride' Bayes object |
| print.gride_evolution | Print 'Gride' evolution object |
| print.gride_mle | Print 'Gride' MLE object |
| print.Hidalgo | Print the Hidalgo object |
| print.hidalgo_psm | Print the summary of the clustering solution |
| print.mus | Print the ratio statistics output |
| print.twonn_bayes | Print 'TWO-NN' Bayes object |
| print.twonn_dec_by | Print 'TWO-NN' evolution object decimated via halving steps |
| print.twonn_dec_prop | Print 'TWO-NN' evolution object decimated via vector of proportions |
| print.twonn_linfit | Print TWO-NN Least Squares output |
| print.twonn_mle | Print 'TWO-NN' MLE output |
| psm_and_cluster | Posterior similarity matrix and partition estimation |
| rgera | The Generalized Ratio distribution |
| 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 |