Three games: proton, frequon and regression. Each one is a console-based data-crunching game for younger and older data scientists.
  Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server.
  In proton you have to solve four data-based puzzles to find the login and password.
  There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools.
  Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try.
  In frequon you will help to perform statistical cryptanalytic attack on a corpus of ciphered messages.
  This time seven sub-tasks are pushing the bar much higher. Do you accept the challenge?
  In regression you will test your modeling skills in a series of eight sub-tasks.
  Try only if ANOVA is your close friend.
  It's a part of Beta and Bit project.
  You will find more about the Beta and Bit project at <https://github.com/BetaAndBit/Charts>.

Fit beta calibration models and obtain calibrated probabilities from
    them.

A family of novel beta mixture models (BMMs) has been developed by Majumdar et al. (2022) <doi:10.48550/arXiv.2211.01938> to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility.

Generates confidence intervals for standardized regression coefficients
    using delta method standard errors for models fitted by lm()
    as described in Yuan and Chan (2011) <doi:10.1007/s11336-011-9224-6>
    and Jones and Waller (2015) <doi:10.1007/s11336-013-9380-y>.
    The package can also be used to generate confidence intervals for
    differences of standardized regression coefficients 
    and as a general approach to performing the delta method.
    A description of the package and code examples
    are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.