Likelihood-Based Intrinsic Dimension Estimators
Plot the simulated MCMC chains for the Bayesian Gride
Plot the evolution of Gride estimates
Plot the simulated bootstrap sample for the MLE Gride
Plot the output of the Hidalgo function
Plot the output of the TWO-NN model estimated via the Bayesian appro...
Plot the output of the TWO-NN model estimated via least squares
Plot the output of the TWO-NN model estimated via the Maximum Likeli...
Auxiliary functions for the Hidalgo model
Posterior similarity matrix and partition estimation
Compute the ratio statistics needed for the intrinsic dimension estima...
The Generalized Ratio distribution
Gride evolution based on Maximum Likelihood Estimation
Gride: the Generalized Ratios ID Estimator
Fit the Hidalgo model
Stratification of the id by an external categorical variable
intRinsic: Likelihood-Based Intrinsic Dimension Estimators
Objects exported from other packages
Generates a noise-free Swiss roll dataset
Estimate the decimated TWO-NN evolution with halving steps or vector...
Estimate the decimated TWO-NN evolution with halving steps or vector...
TWO-NN estimator
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>).