Fit linear, logistic and Cox models regularized with L0, lasso
    (L1), elastic-net (L1 and L2), or net (L1 and Laplacian) penalty, and
    their adaptive forms, such as adaptive lasso / elastic-net and net
    adjusting for signs of linked coefficients. It solves the L0 penalty
    problem by simultaneously selecting regularization parameters and
    performing hard-thresholding or selecting the number of non-zeros. This
    augmented and penalized minimization method provides an approximation
    solution to the L0 penalty problem, but runs as fast as L1 regularization.
    The package uses a one-step coordinate descent algorithm and runs extremely
    fast by taking into account the sparsity structure of coefficients. It can
    handle very high dimensional data and has superior selection performance.

Automated methods to assemble population PK (pharmacokinetic) and
    PKPD (pharmacodynamic) datasets for analysis in 'NONMEM' (non-linear mixed effects
    modeling) by Bauer (2019) <doi:10.1002/psp4.12404>. The package includes functions
    to build datasets from SDTM (study data tabulation module)
    <https://www.cdisc.org/standards/foundational/sdtm>, ADaM (analysis dataset
    module) <https://www.cdisc.org/standards/foundational/adam>, or other dataset
    formats. The package will combine population datasets, add covariates, and
    create documentation to support regulatory submission and internal communication.

Convert several png files into an animated png file.
  This package exports only a single function `apng'. Call the
  apng function with a vector of file names (which should be
  png files) to convert them to a single animated png file.

Finds, prioritizes and deletes erroneous taxa in a phylogenetic tree. This package calculates scores for taxa in a tree. Higher score means the taxon is more erroneous. If the score is zero for a taxon, the taxon is not erroneous. This package also can remove all erroneous taxa automatically by iterating score calculation and pruning taxa with the highest score.