Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
partykit: A Modular Toolkit for Recursive Partytioning in R
Authors: Torsten Hothorn, Achim Zeileis
JMLR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As an example for the visualizations, Figure 1 shows two different trees fitted to the well-known data on survival of passengers on the ill-fated maiden voyage of the RMS Titanic: The left panel shows a CART tree with constant fits learned by rpart and converted to partykit. The right panel shows a MOB tree learned with partykit with a logistic regression for treatment effects in the terminal nodes. |
| Researcher Affiliation | Academia | Torsten Hothorn EMAIL Institut f ur Epidemiologie, Biostatistik und Pr avention, Universit at Z urich Achim Zeileis EMAIL Institut f ur Statistik, Universit at Innsbruck |
| Pseudocode | No | The paper describes the design and functionality of the partykit R package using prose and bullet points, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The partykit package is an add-on package for the R system for statistical computing. It is available from the Comprehensive R Archive Network (CRAN) at http://CRAN.R-project.org/package=partykit and can be installed from within R, e.g., using install.packages. |
| Open Datasets | No | The paper mentions using 'well-known data on survival of passengers on the ill-fated maiden voyage of the RMS Titanic' for illustrative purposes in Figure 1, but it does not provide concrete access information such as a specific link, DOI, repository, or formal citation for this dataset. |
| Dataset Splits | No | The paper mentions fitting models to the Titanic dataset but does not provide any specific information regarding dataset splits (e.g., training, test, or validation percentages or counts). |
| Hardware Specification | No | The paper focuses on describing the 'partykit' R package and its functionalities; it does not provide any specific details about the hardware (e.g., GPU/CPU models, memory specifications) used for development or illustration purposes. |
| Software Dependencies | Yes | It depends on R (at least 2.15.0) as well as the base packages graphics, grid, stats, and the recommended survival. |
| Experiment Setup | No | The paper describes the 'partykit' R package and uses the Titanic dataset for an illustrative visualization. It does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or training configurations for these demonstrations. |