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.

The goal of 'rlowdb' is to provide a lightweight, file-based JSON database. 
    Inspired by 'LowDB' in 'JavaScript', it generates an intuitive interface for
    storing, retrieving, updating, and querying structured data without requiring a full-fledged database system. 
    Ideal for prototyping, small-scale applications, and lightweight data management needs.

The Linear Programming via Regularized Least Squares (LPPinv) is a
             two-stage estimation method that reformulates linear programs as
             structured least-squares problems. Based on the Convex Least
             Squares Programming (CLSP) framework, LPPinv solves linear
             inequality, equality, and bound constraints by (1) constructing a
             canonical constraint system and computing a pseudoinverse
             projection, followed by (2) a convex-programming correction stage
             to refine the solution under additional regularization (e.g.,
             Lasso, Ridge, or Elastic Net). LPPinv is intended for
             underdetermined and ill-posed linear problems, for which standard
             solvers fail.