Package: mlstm 0.1.6
Tomoya Himeno
mlstm: Multilevel Supervised Topic Models with Multiple Outcomes
Fits latent Dirichlet allocation (LDA), supervised topic models, and multilevel supervised topic models for text data with multiple outcome variables. Core estimation routines are implemented in C++ using the 'Rcpp' ecosystem. For topic models, see Blei et al. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>. For supervised topic models, see Blei and McAuliffe (2007) <https://papers.nips.cc/paper_files/paper/2007/hash/d56b9fc4b0f1be8871f5e1c40c0067e7-Abstract.html>.
Authors:
mlstm_0.1.6.tar.gz
mlstm_0.1.6.zip(r-4.7)mlstm_0.1.6.zip(r-4.6)mlstm_0.1.6.zip(r-4.5)
mlstm_0.1.6.tgz(r-4.6-x86_64)mlstm_0.1.6.tgz(r-4.6-arm64)mlstm_0.1.6.tgz(r-4.5-x86_64)mlstm_0.1.6.tgz(r-4.5-arm64)
mlstm_0.1.6.tar.gz(r-4.7-arm64)mlstm_0.1.6.tar.gz(r-4.7-x86_64)mlstm_0.1.6.tar.gz(r-4.6-arm64)mlstm_0.1.6.tar.gz(r-4.6-x86_64)
mlstm_0.1.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
mlstm/json (API)
NEWS
| # Install 'mlstm' in R: |
| install.packages('mlstm', repos = c('https://thimeno1993.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/thimeno1993/mlstm/issues
Pkgdown/docs site:https://thimeno1993.github.io
Last updated from:d345ec5b4b. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 150 | ||
| linux-devel-x86_64 | OK | 154 | ||
| source / vignettes | OK | 221 | ||
| linux-release-arm64 | OK | 161 | ||
| linux-release-x86_64 | OK | 153 | ||
| macos-release-arm64 | OK | 157 | ||
| macos-release-x86_64 | OK | 417 | ||
| macos-oldrel-arm64 | OK | 168 | ||
| macos-oldrel-x86_64 | OK | 345 | ||
| windows-devel | OK | 146 | ||
| windows-release | OK | 170 | ||
| windows-oldrel | OK | 150 | ||
| wasm-release | OK | 136 |
Exports:eLDA_pass_b_fastinit_mod_from_countrun_lda_gibbsrun_mlstm_virun_stm_viset_threadsstm_multi_hier_vi_parallelstm_vi_parallel
Dependencies:BHdata.tablelatticeMatrixRcppRcppArmadilloRcppParallel
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| One Gibbs sampling sweep for LDA (collapsed) using document–term list. | eLDA_pass_b_fast |
| Initialize LDA/STM state from a (d, v, c) sparse count matrix. | init_mod_from_count |
| Collapsed LDA Gibbs sampling for sparse (d, v, c) triplet data. | run_lda_gibbs |
| Multi-level supervised topic model (MLSTM) via variational inference. | run_mlstm_vi |
| Supervised topic model (STM) variational inference with ELBO-based convergence. | run_stm_vi |
| Set threading options for STM/MLSTM computations | set_threads |
| Variational inference for multi-output supervised topic models with hierarchical prior. | stm_multi_hier_vi_parallel |
| Variational inference for supervised LDA (single continuous response). | stm_vi_parallel |