Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET) design is a model-assisted oncology phase I/II trial design, aiming to establish an optimal biological dose accounting for efficacy and toxicity in the framework of dose-finding. Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). 'boinet' is a package to implement the BOIN-ET design family and supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET, where users can choose design parameters in flexible and straightforward ways depending on their own application.

Perform seasonal adjustment of weekly data. The package offers a user-friendly interface for computing seasonally adjusted estimates of weekly data and also includes diagnostic tools to assess the quality of the adjustments. Furthermore, it incorporates tools uniquely tailored to the specific characteristics of Israeli data. The method is described in more detail in Ginker (2023) <DOI:10.13140/RG.2.2.12221.44000>.

Provides an interface to Bank of Japan <https://www.boj.or.jp>
  statistics.

Implements the bolasso algorithm for consistent variable selection 
    and estimation accuracy. Includes support for many parallel backends via the 
    future package. For details see: Bach (2008),
    'Bolasso: model consistent Lasso estimation through the bootstrap', 
    <arXiv:0804.1302>.

A programmatic interface to the Web Service methods provided by
    Bold Systems (<http://www.boldsystems.org/>) for genetic 'barcode' data.
    Functions include methods for searching by sequences by taxonomic names,
    ids, collectors, and institutions; as well as a function for searching
    for specimens, and downloading trace files.