intRinsic - Likelihood-Based Intrinsic Dimension Estimators

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>).

Last updated 5 months ago

cppopenmp

4.11 score 13 stars 10 scripts 265 downloads

SANple - Fitting Shared Atoms Nested Models via Markov Chains Monte Carlo

Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), and hybrid finite-infinite models. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, D’Angelo, Denti (2024) <doi:10.1214/24-BA1458>.

Last updated 5 months ago

cppopenmp

3.48 score 3 scripts 454 downloads

SANvi - Fitting Shared Atoms Nested Models via Variational Bayes

An efficient tool for fitting the nested common and shared atoms models using variational Bayes approximate inference for fast computation. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite model. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, D’Angelo, Canale, Yu, Guindani (2023) <doi:10.1111/biom.13626>.

Last updated 8 months ago

openblascppopenmp

3.30 score 1 stars 1 scripts 161 downloads