'BabyTime' is an application for tracking infant and toddler
    care activities like sleeping, eating, etc. This package will take the
    outputted .zip files and parse it into a usable list object with
    cleaned data. It handles malformed and incomplete data gracefully and
    is designed to parse one directory at a time.

The BACCO bundle of packages is replaced by the BACCO
 package, which provides a vignette that illustrates the constituent
 packages (emulator, approximator, calibrator) in use.

Implements the Bayesian Augmented Control (BAC, a.k.a. Bayesian historical data borrowing) method under clinical trial setting by calling 'Just Another Gibbs Sampler' ('JAGS') software. In addition, the 'BACCT' package evaluates user-specified decision rules by computing the type-I error/power, or probability of correct go/no-go decision at interim look. The evaluation can be presented numerically or graphically. Users need to have 'JAGS' 4.0.0 or newer installed due to a compatibility issue with 'rjags' package. Currently, the package implements the BAC method for binary outcome only. Support for continuous and survival endpoints will be added in future releases. We would like to thank AbbVie's Statistical Innovation group and Clinical Statistics group for their support in developing the 'BACCT' package.

Provides tools to facilitate the access and processing of data 
    from the Central Bank of Brazil API. The package allows users 
    to retrieve economic and financial data, transforming them into usable 
    tabular formats for further analysis. The data is obtained from the 
    Central Bank of Brazil API: <https://api.bcb.gov.br/dados/serie/bcdata.sgs.{series_code}/dados?formato=json&dataInicial={start_date}&dataFinal={end_date}>.

Provides the design of multi-group phase
    II clinical trials with binary outcomes using the hierarchical Bayesian
    classification and information sharing (BaCIS) model. Subgroups are classified
    into two clusters on the basis of their outcomes mimicking the hypothesis
    testing framework. Subsequently, information sharing takes place within
    subgroups in the same cluster, rather than across all subgroups. This method can
    be applied to the design and analysis of multi-group clinical trials with binary
    outcomes. Reference: Nan Chen and J. Jack Lee (2019) <doi:10.1002/bimj.201700275>.