Real-time quantitative polymerase chain reaction (qPCR) data
    sets by Boggy et al. (2008) <doi:10.1371/journal.pone.0012355>. This package
    provides a dilution series for one PCR target: a random sequence that
    minimizes secondary structure and off-target primer binding. The data set
    is a six-point, ten-fold dilution series.
    For each concentration there are two replicates. Each amplification curve
    is 40 cycles long. Original raw data file:
    <https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0012355.s004>.

Provides deterministic two- and three-dimensional boids and swarm
    simulations for R. The package implements Reynolds-style separation,
    alignment, and cohesion rules with optional obstacles, attractors,
    predators, species parameters, and reproducible frame export. Simulation
    state is renderer-neutral; optional adapters can hand frame data to
    visualization packages such as 'ggWebGL'. The model follows Reynolds
    (1987) <doi:10.1145/37402.37406>.

Generic Extraction of main text content from HTML files; removal
    of ads, sidebars and headers using the boilerpipe 
    <https://github.com/kohlschutter/boilerpipe> Java library. The
    extraction heuristics from boilerpipe show a robust performance for a wide
    range of web site templates.

Managing and generating standardised text for methods and results sections of scientific reports. It handles template variable substitution and supports hierarchical organisation of text through dot-separated paths. Databases are stored as JSON by default for version control and cross-language compatibility; trusted legacy RDS databases remain readable through import and migration utilities.

The Bayesian optimal interval (BOIN) design is a novel phase I
    clinical trial design for finding the maximum tolerated dose (MTD). It can be
    used to design both single-agent and drug-combination trials. The BOIN design
    is motivated by the top priority and concern of clinicians when testing a new
    drug, which is to effectively treat patients and minimize the chance of exposing
    them to subtherapeutic or overly toxic doses. The prominent advantage of the
    BOIN design is that it achieves simplicity and superior performance at the same
    time. The BOIN design is algorithm-based and can be implemented in a simple
    way similar to the traditional 3+3 design. The BOIN design yields an average
    performance that is comparable to that of the continual reassessment method
    (CRM, one of the best model-based designs) in terms of selecting the MTD, but
    has a substantially lower risk of assigning patients to subtherapeutic or overly
    toxic doses. For tutorial, please check Yan et al. (2020) <doi:10.18637/jss.v094.i13>.