Design parameters of the optimal two-period multiarm platform design
    (controlling for either family-wise error rate or pair-wise error rate) can be calculated
    using this package, allowing pre-planned deferred arms to be added during the trial. More
    details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J.
    (2022) "An optimal two-period multiarm platform design with new experimental arms added
    during the trial". Manuscript submitted for publication. For additional references: Dunnett,
    C. W. (1955) <doi:10.2307/2281208>.

Data and analysis from an experiment with improving touch 
    typing speed, using the tDCS PlatoWork headset produced by PlatoScience.

It creates a lattice plot to visualize panel or longitudinal data. The observed values are plotted as dots and the fitted values as lines, both against time. The plot is customizable and easy to edit, even if you do not know how to construct a lattice plot from scratch.

Enhances dynamic effect plots as suggested in Freyaldenhoven and Hansen (2026) <https://simonfreyaldenhoven.github.io/papers/Plausible_bounds.pdf>. Data-driven smoothing delivers a smooth estimated path with potentially improved point estimation properties and confidence regions covering a surrogate that can be substantially tighter than conventional pointwise or uniform bands.

Extends the popular 'lavaan' package by adding penalized
    estimation capabilities. It supports penalty on individual parameters
    as well as the difference between parameters.

Games that can be played in the R console.  Includes coin
    flip, hangman, jumble, magic 8 ball, poker, rock paper scissors, shut
    the box, spelling bee, and 2048.

Create an interactive pizza chart visualizing a specific player's statistics across various attributes in a sports dataset. The chart is constructed based on input parameters: 'data', a dataframe containing player data for any sports; 'player_stats_col', a vector specifying the names of the columns from the dataframe that will be used to create slices in the pizza chart, with statistics ranging between 0 and 100; 'name_col', specifying the name of the column in the dataframe that contains the player names; and 'player_name', representing the specific player whose statistics will be visualized in the chart, serving as the chart title.