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 and forecasting of weekly data.
    The package provides a user-friendly interface for computing seasonally
    adjusted estimates and forecasts of weekly time series and includes
    functions for the construction of country-specific prior adjustment
    variables, as well as diagnostic tools to assess the quality of the
    adjustments. The methodology is described in more detail in
    Ginker (2024) <doi:10.13140/RG.2.2.12221.44000>.

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

An unofficial, tidy, rate-limited interface to the 'Bank of Japan
    Time-Series Data Search' API. Search series metadata, retrieve observations
    by series code or hierarchy, follow pagination automatically, and work with
    normalized long or wide data frames. The API does not require an API key.

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', 
    <doi:10.48550/arXiv.0804.1302>.