# Likelihood-Based Intrinsic Dimension Estimators

## Documentation for package ‘intRinsic’ version 1.1.2

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

autoplot.gride_bayes | Plot the simulated MCMC chains for the Bayesian 'Gride'
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.mus_Nq | 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
twonn_decimation | Estimate the decimated 'TWO-NN' evolution with halving steps or vector of proportions