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>).

Functions for the determination of optimally robust influence curves and
            estimators for preprocessing omics data, in particular gene expression data (Kohl
            and Deigner (2010), <doi:10.1186/1471-2105-11-583>).

A framework for estimating ensembles of meta-analytic, meta-regression, and 
    multilevel models (assuming either presence or absence of the effect, heterogeneity,
    publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to 
    combine the competing meta-analytic models into a model ensemble, weights 
    the posterior parameter distributions based on posterior model probabilities 
    and uses Bayes factors to test for the presence or absence of the
    individual components (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 define a wide range of prior distributions for the effect size, heterogeneity, 
    publication bias (including selection models and PET-PEESE), and moderator components. 
    The package provides convenient functions for summary, visualizations, and fit diagnostics.