Built on top of the 'glmnet' library by Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01>, the 'plasso' package follows Knaus (2022) <doi:10.1093/ectj/utac015> and comes up with two functions that estimate least squares Lasso and Post-Lasso models.
  The plasso() function adds coefficient paths for a Post-Lasso model to the standard 'glmnet' output.
  On top of that cv.plasso() cross-validates the coefficient paths for both the Lasso and Post-Lasso model and provides optimal hyperparameter values for the penalty term lambda.

Tools for interacting with data from experiments done in microtiter
    plates. Easily read in plate-shaped data and convert it to tidy format, 
    combine plate-shaped data with tidy data, and view tidy data in plate shape.

Collection of functions for working with multi-well microtitre
    plates, mainly 96, 384 and 1536 well plates.

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.

We present 'Platypus', an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. 'Platypus' provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology. This R version of 'Platypus' is part of the 'ePlatypus' ecosystem for computational analysis of immunogenomics data: Yermanos et al. (2021) <doi:10.1093/nargab/lqab023>, Cotet et al. (2023) <doi:10.1093/bioinformatics/btad553>.