Package: telescope 0.2-2

telescope: Bayesian Mixtures with an Unknown Number of Components

Fits Bayesian finite mixtures with an unknown number of components using the telescoping sampler and different component distributions. For more details see Frühwirth-Schnatter et al. (2021) <doi:10.1214/21-BA1294>, Malsiner-Walli et al. (in press) <doi:10.1007/s11634-025-00640-x> and Malsiner-Walli et al. (2026) <doi:10.48550/arXiv.2603.00277>.

Authors:Gertraud Malsiner-Walli [aut, cre], Bettina Grün [aut], Sylvia Frühwirth-Schnatter [aut]

telescope_0.2-2.tar.gz
telescope_0.2-2.zip(r-4.7-any)telescope_0.2-2.zip(r-4.6-any)telescope_0.2-2.zip(r-4.5-any)
telescope_0.2-2.tgz(r-4.6-any)telescope_0.2-2.tgz(r-4.5-any)
telescope_0.2-2.tar.gz(r-4.7-any)telescope_0.2-2.tar.gz(r-4.6-any)
telescope_0.2-2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
telescope/json (API)

# Install 'telescope' in R:
install.packages('telescope', repos = c('https://gertraudmalsinerwalli.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • SimData - Simulated Multivariate Binary Data

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.78 score 4 scripts 363 downloads 12 exports 24 dependencies

Last updated from:c396a9ba41. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK134
source / vignettesOK349
linux-releaseOK137
macos-releaseOK167
macos-oldrelOK159
windows-develOK99
windows-releaseOK84
windows-oldrelOK69
wasm-releaseOK108

Exports:identifyLCAMixtureidentifyMixtureplotBubbleplotScatterpriorOnAlpha_specpriorOnE0_specpriorOnK_specsampleLCAsampleLCAMixturesampleMultNormMixturesamplePoisMixturesampleUniNormMixture

Dependencies:abindbayesmcodadigestDirichletRegextraDistrFormulagenericslatticeMASSMatrixMatrixModelsmaxLikmcmcMCMCpackmiscToolsmvtnormquantregRcppRcppArmadillosandwichSparseMsurvivalzoo

Bayesian Mixture of Latent Class Analysis Models with the Telescoping Sampler
Introduction | Data | Model specification | Specification of the MCMC simulation and prior parameters | MCMC sampling | Convergence diagnostics of the run | Identification of the mixture model | Step 1: Estimate $K_+$ and $K$ | Step 2: Extracting the draws with exactly $\hat{K}_+$ non-empty components | Step 3: Clustering and relabeling of the MCMC draws | Step 4: Estimating data cluster specific parameters and determining the final partition | Step 4: Visualizing the posterior component occurrence and precision distributions mu_k and phi_j. | References
Rendered fromBayesian_LCA_mixtures.Rmdusingknitr::rmarkdown

Last update: 2026-04-10
Started: 2025-01-23

Using CliPS to Identify a Dynamic Mixture of Finite Mixtures of Multivariate Gaussian Distributions -- Case Study: Diabetes Data
Introduction | Step 1: Define a mixture model | Step 2: MCMC sampling from the posterior | Step 2b: Post-processing the MCMC draws | Steps 3-4-5: Clustering of the draws | Step 6: Characterization of the cluster distributions | References
Rendered fromCliPS_diabetes.Rmdusingknitr::rmarkdown

Last update: 2026-04-10
Started: 2026-04-10

Bayesian Latent Class Analysis Models with the Telescoping Sampler
Introduction | The fear data set | Model specification | Specification of the MCMC simulation and prior parameters | MCMC sampling | Convergence diagnostics of the run | Identification of the mixture model | Step 1: Estimate $K_+$ and $K$ | Step 2: Extracting the draws with exactly $\hat{K}_+$ non-empty components | Step 3: Clustering and relabeling of the MCMC draws | Step 4: Estimating data cluster specific parameters and determining the final partition | References
Rendered fromBayesian_LCA.Rmdusingknitr::rmarkdown

Last update: 2025-01-23
Started: 2024-06-16

Bayesian Multivariate Gaussian Mixtures with the Telescoping Sampler
Introduction | The thyroid data set | Model specification | Specification of the simulation and prior parameters | MCMC sampling | Convergence diagnostics of the run | Identification of the mixture model | Step 1: Estimating $K_+$ and $K$ | Step 2: Extracting the draws with exactly $\hat{K}_+$ non-empty components | Step 3: Clustering and relabeling of the MCMC draws in the point process representation | Step 4: Estimating data cluster specific parameters and determining the final partition | Step 5: Visualizing the estimated classification | References
Rendered fromBayesian_mult_Gaussian_mixtures.Rmdusingknitr::rmarkdown

Last update: 2025-01-23
Started: 2024-06-16

Bayesian Poisson Mixtures with the Telescoping Sampler
Introduction | The eye data set | Model specification | Specification of the simulation and prior parameters | MCMC sampling | Convergence diagnostics of the run | Identification of the mixture model | Step 1: Estimate $K_+$ and $K$ | Step 2: Extract those draws which correspond to samples with exactly $\hat{K}_+$ filled components | Step 3: Clustering and relabeling of the MCMC draws, with $\hat{K}_+$ being the estimated number of data clusters | References
Rendered fromBayesian_Poisson_mixtures.Rmdusingknitr::rmarkdown

Last update: 2025-01-23
Started: 2024-06-16

Bayesian Univariate Gaussian Mixtures with the Telescoping Sampler
Introduction | The Galaxy data set | Model specification | Specification of the MCMC simulation and prior parameters | MCMC sampling | Convergence diagnostics of the run | Identification of the mixture model | Step 1: Estimating $K_+$ and $K$ | Step 2: Extracting the draws with exactly $\hat{K}_+$ non-empty components | Step 3: Clustering and relabeling of the MCMC draws in the point process representation | Step 4: Estimating data cluster specific parameters and determing the final partition | Step 5: Visualizing the estimated classification | References
Rendered fromBayesian_univ_Gaussian_mixtures.Rmdusingknitr::rmarkdown

Last update: 2025-01-23
Started: 2024-06-16