Interface with the 'Brickset' API
    <https://brickset.com/article/52664/api-version-3-documentation> for
    getting data about LEGO sets. Data sets that can be used for teaching
    and learning without the need of a 'Brickset' account and API key are
    also included. Includes all LEGO since through the end of 2023.

Collection of utilities that improve using 'Databricks' from R. 
  Primarily functions that wrap specific 'Databricks' APIs
  (<https://docs.databricks.com/api>), 'RStudio' connection pane support, quality
  of life functions to make 'Databricks' simpler to use.

An implementation of the bridge distribution with logit-link in
    R. In Wang and Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate
    bridge distribution was derived as the distribution of the random intercept that
    'bridged' a marginal logistic regression and a conditional logistic regression.
    The conditional and marginal regression coefficients are a scalar multiple
    of each other. Such is not the case if the random intercept distribution was
    Gaussian.

BRIC-seq is a genome-wide approach for determining RNA stability in mammalian cells.
    This package provides a series of functions for performing quality check of your BRIC-seq data,
    calculation of RNA half-life for each transcript and comparison of RNA half-lives between two conditions.

Provides functions for estimating marginal likelihoods, Bayes
    factors, posterior model probabilities, and normalizing constants in general,
    via different versions of bridge sampling (Meng & Wong, 1996, 
    <http://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>).
    Gronau, Singmann, & Wagenmakers (2020) <doi:10.18637/jss.v092.i10>.

Implements bridge models for nowcasting and forecasting macroeconomic variables by linking high-frequency indicator variables (e.g., monthly data) to low-frequency target variables (e.g., quarterly GDP). Simplifies forecasting and aggregating indicator variables to match the target frequency, enabling timely predictions ahead of official data releases. For more on bridge models, see Baffigi, A., Golinelli, R., & Parigi, G. (2004) <doi:10.1016/S0169-2070(03)00067-0>, Burri (2023) <https://www5.unine.ch/RePEc/ftp/irn/pdfs/WP23-02.pdf> or Schumacher (2016) <doi:10.1016/j.ijforecast.2015.07.004>.