ptsuite: Fast Tail Index Estimation for Power Law Distributions in R
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Power law distributions, in particular Pareto distributions, describe data across diverse areas of study. We have developed a package, ptsuite, in R to estimate the tail index for such datasets which: a) uses a variety of estimation methods; b) focuses on speed (in particular with large datasets); c) is accurate and d) is easy to use. The package is also able to generate Pareto data as well as conduct both heuristic and statistical tests to check if data is Paretian. We tested ptsuite against similar R packages for speed of tail index estimation and found that our package is indeed faster. The tail index estimates produced by the package are accurate. The package is easy to use as all functions can be called with one line of code and a small number of argument references. To date the package has been downloaded over 12,500 times from the CRAN repository 4. Finally we remark that the authors have used the package in research applications - e.g. (Munasinghe et. al , 2019).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it