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This article illustrates intRinsic, an R package that implements novelstate-of-the-art likelihood-based estimators of the intrinsic dimension of adataset, an essential quantity for most dimensionality reduction techniques. Inorder to make these novel estimators easily accessible, the package contains asmall number of high-level functions that rely on a broader set of efficient,low-level routines. Generally speaking, intRinsic encompasses models that fallinto two categories: homogeneous and heterogeneous intrinsic dimensionestimators. The first category contains the two nearest neighbors estimator, amethod derived from the distributional properties of the ratios of thedistances between each data point and its first two closest neighbors. Thefunctions dedicated to this method carry out inference under both thefrequentist and Bayesian frameworks. In the second category, we find theheterogeneous intrinsic dimension algorithm, a Bayesian mixture model for whichan efficient Gibbs sampler is implemented. After presenting the theoreticalbackground, we demonstrate the performance of the models on simulated datasets.This way, we can facilitate the exposition by immediately assessing thevalidity of the results. Then, we employ the package to study the intrinsicdimension of the Alon dataset, obtained from a famous microarray experiment.Finally, we show how the estimation of homogeneous and heterogeneous intrinsicdimensions allows us to gain valuable insights into the topological structureof a dataset.

intRinsic: an R Package for Model-Based Estimation of the Intrinsic Dimension of a Dataset