Introduction to the tidylda package | Tidy Methods | Other Notable Features | Discussion | Installation | Example
This package is considered a duplicate. The official version of this package is found at:https://tommyjones.r-universe.dev/tidylda
Package: tidylda 0.1.1
Tommy Jones
tidylda: Latent Dirichlet Allocation Using 'tidyverse' Conventions
Implements an algorithm for Latent Dirichlet Allocation (LDA), Blei et al. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>, using style conventions from the 'tidyverse', Wickham et al. (2019)<doi:10.21105/joss.01686>, and 'tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>. Fitting is done via 'warpLDA', a Metropolis-Hastings sampler, Chen et al. (2016) <doi:10.48550/arXiv.1510.08628>. Also implements several novel features for LDA such as guided models and transfer learning.
Authors:
tidylda_0.1.1.tar.gz
tidylda_0.1.1.zip(r-4.7-x86_64)tidylda_0.1.1.zip(r-4.7-arm64)tidylda_0.1.1.zip(r-4.6-x86_64)tidylda_0.1.1.zip(r-4.6-arm64)
tidylda_0.1.1.tar.gz(r-4.7-arm64)tidylda_0.1.1.tar.gz(r-4.7-x86_64)tidylda_0.1.1.tar.gz(r-4.6-arm64)tidylda_0.1.1.tar.gz(r-4.6-x86_64)
tidylda_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tidylda/json (API)
| # Install 'tidylda' in R: |
| install.packages('tidylda', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/tommyjones/tidylda/issues
Datasets:
- nih_sample - Abstracts and metadata from NIH research grants awarded in 2014
- nih_sample_dtm - Abstracts and metadata from NIH research grants awarded in 2014
Last updated from:b11df39313. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 181 | ||
| linux-devel-x86_64 | OK | 173 | ||
| source / vignettes | OK | 255 | ||
| linux-release-arm64 | OK | 178 | ||
| linux-release-x86_64 | OK | 225 | ||
| windows-devel-arm64 | OK | 295 | ||
| windows-devel-x86_64 | OK | 200 | ||
| windows-release-arm64 | OK | 287 | ||
| windows-release-x86_64 | OK | 169 | ||
| wasm-release | OK | 142 |
Exports:augmentcalc_prob_coherenceglanceposteriorrefittidytidylda
Dependencies:clicpp11dplyrgenericsgluegtoolsjaneaustenrlatticelifecyclemagrittrMatrixmvrsquaredpillarpkgconfigpurrrR6RcppRcppArmadilloRcppProgressRcppThreadrlangSnowballCstringistringrtibbletidyrtidyselecttidytexttokenizersutf8vctrswithr
Introduction to tidylda
Last update: 2026-08-28
Started: 2023-07-14
Transfer Learning with LDA (tLDA)
Fine Tuning Latent Dirichlet Allocation for Transfer Learning | Contribution | tLDA | The Model | Selecting the prior weight | The tidylda Implementation of tLDA | Aligning Vocabulary | Adding New Topics | Initializing $\boldsymbol{Cd}^{(t)}$ and $\boldsymbol{Cv}^{(t)}$ | Partitioning the Posterior to Collapse $K$ Weights ($\omega_k$) Into One, $a$
Last update: 2026-08-28
Started: 2023-07-14
Probabilistic Coherence
Last update: 2023-07-14
Started: 2023-07-14
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Augment method for 'tidylda' objects | augment.tidylda |
| Probabilistic coherence of topics | calc_prob_coherence |
| Glance method for 'tidylda' objects | glance.tidylda |
| Abstracts and metadata from NIH research grants awarded in 2014 | nih nih_sample nih_sample_dtm |
| Draw from the marginal posteriors of a tidylda topic model | posterior posterior.tidylda |
| Get predictions from a Latent Dirichlet Allocation model | predict.tidylda |
| Print Method for tidylda | print.tidylda |
| Update a Latent Dirichlet Allocation topic model | refit.tidylda |
| Tidy a matrix from a 'tidylda' topic model | tidy.matrix tidy.tidylda |
| Fit a Latent Dirichlet Allocation topic model | tidylda |