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.

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>.

Facilitates retrieval, transformation and analysis of the data
    from the Barcode of Life Data Systems (BOLD) database <https://boldsystems.org/>. 
    This package allows both public and private user data to be easily downloaded into the R
    environment using a variety of inputs such as: IDs (processid, sampleid), BINs, dataset codes, 
    project codes, taxonomy, geography etc. It provides frictionless data conversion  
    into formats compatible with other R-packages and third-party tools, 
    as well as functions for sequence alignment & clustering, biodiversity analysis and spatial mapping.

Provides data import and offers 3 daily snapshot functions from securities of varying prices traded on the Bolivian Securities Exchange, website <https://www.bbv.com.bo/>. The snapshots include a detailed list, scatter plot correlation, and descriptive statistics table for the securities.

A set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.

A set of R functions and data sets for the book "Understanding Computational Bayesian Statistics." This book was written by Bill (WM) Bolstad and published in 2009 by John Wiley & Sons (ISBN 978-0470046098).