Provides half-normal plots, reference plots, and Pareto plots
    of effects from an unreplicated experiment, along with various 
    pseudo-standard-error measures, simulated reference distributions, 
    and other tools. Many of these methods are described in 
    Daniel C. (1959) <doi:10.1080/00401706.1959.10489866> and/or
    Lenth R.V. (1989) <doi:10.1080/00401706.1989.10488595>, but some new
    approaches are added and integrated in one package.

Wraps the 'unrtf' utility <https://www.gnu.org/software/unrtf/> to 
    extract text from RTF files. Supports document conversion to HTML, LaTeX or
    plain text. Output in HTML is recommended because 'unrtf' has limited 
    support for converting between character encodings.

Automatically converts language-specific verbal information, e.g., "1st half of the 19th century," to its standardized numerical counterparts, e.g., "1801-01-01/1850-12-31." It follows the recommendations of the 'MIDAS' ('Marburger Informations-, Dokumentations- und Administrations-System'), see <doi:10.11588/artdok.00003770>.

Reconstructs all possible raw data that could have led to reported
    summary statistics. Provides a wrapper for the 'Rust' implementation of the
    'CLOSURE' algorithm.

Tools for clustering individualized survival curves using the
    Partitioning Around Medoids (PAM) algorithm, with monotonic enforcement,
    optional smoothing, weighted distances (L1/L2), automatic K selection via
    silhouette width, prediction for new curves, basic stability checks, and
    plotting helpers. The clustering strategy follows Kaufman and Rousseeuw
    (1990, ISBN:0471878766).

Performs a test for second-order stationarity of time series based
    on unsystematic sub-samples.

Hubbell's Unified Neutral Theory of Biodiversity.

Calculates a Mahalanobis distance for every row of a set of
    outcome variables (Mahalanobis, 1936
    <doi:10.1007/s13171-019-00164-5>). The conditional Mahalanobis
    distance is calculated using a conditional covariance matrix (i.e., a
    covariance matrix of the outcome variables after controlling for a set
    of predictors). Plotting the output of the cond_maha() function can
    help identify which elements of a profile are unusual after
    controlling for the predictors.