Perform robust estimation and inference in platform trials and other master protocol trials.
  Yuhan Qian, Yifan Yi, Jun Shao, Yanyao Yi, Gregory Levin, Nicole Mayer-Hamblett, Patrick J. Heagerty, Ting Ye (2025) <doi:10.48550/arXiv.2411.12944>.

Execute API calls to the RobinHood <https://robinhood.com> investing platform. Functionality includes accessing account data and current holdings, retrieving investment statistics and quotes, placing and canceling orders, getting market trading hours, searching investments by popular tag, and interacting with watch lists.

Client for the Ocean Biodiversity Information System (<https://obis.org>).

Implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) <arXiv:1204.3358> and Agamennoni et al. (2018) <doi:10.1109/ICRA.2011.5979605>, the innovative outlier robust filter of Ruckdeschel et al. (2014) <arXiv:1204.3358>, as well as the innovative and additive outlier robust filter of Fisch et al. (2020) <arXiv:2007.03238>.

Functions for the determination of optimally robust influence curves and
            estimators in case of normal location and/or scale (see Chapter 8 in Kohl (2005)
            <https://epub.uni-bayreuth.de/839/2/DissMKohl.pdf>).

A framework for Bayesian meta-analysis, including model estimation,
      prior specification, model comparison, prediction, summaries, visualizations,
      and diagnostics. The package fits single and model-averaged meta-analytic,
      meta-regression, multilevel, publication bias adjusted, and generalized linear 
      mixed models The model-averaged meta-analytic models combine competing models 
      based on their predictive performance, weight inference by posterior model probabilities, 
      and test model components using Bayes factors (e.g., effect vs. no effect; 
      Bartoš et al., 2022, <doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022,
      <doi:10.1037/met0000405>; Bartoš et al., 2025, <doi:10.1037/met0000737>).
      Users can specify flexible prior distributions for effect sizes, heterogeneity,
      publication bias (including selection models and PET-PEESE), and moderators.