Package: tamd 1.0.2
Ernest Fokoue
tamd: Transcendental Algorithm for Mixtures of Distributions
Implements the Transcendental Algorithm for Mixtures of Distributions (TAMD), a penalized likelihood framework for fitting finite Gaussian mixture models. TAMD augments the Expectation-Maximization (EM) algorithm with analytic barrier terms built from the Hellinger affinity that diverge on the singular locus, actively preventing component coalescence and weight degeneracy. Provides the core TAMD fitting function, closed-form Hellinger affinity and gradient computations, the Transcendental Affinity Criterion (TAC) for geometry-aware model selection, the regularity index rho (a scalar diagnostic for mixture fit quality), and reproduction scripts for all simulation studies. Methods are described in Fokoue (2024) <doi:10.48550/arXiv.2602.03889>. See also Titterington, Smith and Makov (1985, ISBN:0-471-90510-4) and Watanabe (2009, ISBN:978-0-521-86408-7).
Authors:
tamd_1.0.2.tar.gz
tamd_1.0.2.tar.gz(r-4.7-any)tamd_1.0.2.tar.gz(r-4.6-any)
tamd_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
tamd/json (API)
| # Install 'tamd' in R: |
| install.packages('tamd', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:dd31d14bb5. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 111 | ||
| source / vignettes | OK | 150 | ||
| linux-release-x86_64 | OK | 122 | ||
| wasm-release | OK | 95 |
Exports:compute_aiccompute_biccompute_taccompute_tichellinger_affinityhellinger_affinity_matrixlabel_matchregularity_indexrlct_gaussianseparation_gradient_covseparation_gradient_meansimulate_gmmtamdtamd_select
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| AIC for Gaussian Mixture Models | compute_aic |
| BIC for Gaussian Mixture Models | compute_bic |
| Transcendental Affinity Criterion (TAC) | compute_tac |
| Transcendental Information Criterion (TIC) | compute_tic |
| Hellinger Affinity Between Two Gaussian Distributions | hellinger_affinity |
| Pairwise Hellinger Affinity Matrix | hellinger_affinity_matrix |
| Label-Matched Parameter Comparison | label_match |
| Plot a tamd Object | plot.tamd |
| Print a tamd Object | print.tamd |
| Regularity Index (Titterington Theorem) | regularity_index |
| Real Log-Canonical Threshold for Gaussian Mixtures | rlct_gaussian |
| Separation Barrier Gradients | separation_gradient_cov separation_gradient_mean |
| Simulate Data from a Gaussian Mixture Model | simulate_gmm |
| Summarize a tamd Object | summary.tamd |
| Fit a Gaussian Mixture Model via TAMD | tamd |
| Automated Model Order Selection via TAC | tamd_select |