Provides a set of functions for data manipulation with
    list objects, including mapping, filtering, grouping, sorting,
    updating, searching, and other useful functions. Most functions
    are designed to be pipeline friendly so that data processing with
    lists can be chained.

Listings are often part of the submission of clinical trial
    data in regulatory settings.  We provide a framework for the specific
    formatting features often used when displaying large datasets in that
    context.

Provides an interface to access and analyze data from
    'DeFiLlama'<https://defillama.com>. This package simplifies the
    process of fetching and manipulating 'DeFiLlama' data for further
    analysis and visualization.

Data driven approach for robust regression estimation in homoscedastic and heteroscedastic context. See Wang et al. (2007), <doi:10.1198/106186007X180156> regarding homoscedastic framework.

Estimates robust rank-based fixed effects and predicts robust
    random effects in two- and three- level random effects nested models.
    The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.

R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.

A very lightweight package that writes out log messages in an opinionated way.
  Simpler and lighter than other logging packages, 'rlog' provides a compact feature set that
  focuses on getting the job done in a Unix-like way.

Processing logical operations such as AND/OR/NOT operations
    dynamically. It also handles nesting in the operations.

An implementation to compute an optimal adaptive allocation rule
    using deep reinforcement learning in a dose-response study
    (Matsuura et al. (2022) <doi:10.1002/sim.9247>).
    The adaptive allocation rule can directly optimize a performance metric,
    such as power, accuracy of the estimated target dose, or mean absolute error
    over the estimated dose-response curve.