Estimators for two functionals used to detect Gamma, Pareto or Lognormal distributions, as well as distributions exhibiting similar tail behavior, as introduced by Iwashita and Klar (2023) <doi:10.1111/stan.12316> and 
            Klar (2024) <doi:10.1080/00031305.2024.2413081>. 
            One of these functionals, g, originally proposed by Asmussen and Lehtomaa (2017) <doi:10.3390/risks5010010>, distinguishes between log-convex and log-concave tail behavior. 
            Furthermore the characterization of the lognormal distribution is based on the work of Mosimann (1970) <doi:10.2307/2284599>.
            The package also includes methods for visualizing these estimators and their associated confidence intervals across various threshold values.

Implements the tail-rank statistic for selecting biomarkers
  from a microarray data set, an efficient nonparametric test focused
  on the distributional tails. See
  <https://gitlab.com/krcoombes/coombeslab/-/blob/master/doc/papers/tolstoy-new.pdf>.

When plotting treated-minus-control differences, after-minus-before changes, or difference-in-differences, the ttrans() function symmetrically transforms the positive and negative tails to aid plotting.  The package includes an observational study with three control groups and an unaffected outcome; see Rosenbaum (2022) <doi:10.1080/00031305.2022.2063944>.

A tool to help create shiny apps for selecting and annotating 
    elements of images. Users must supply images, questions, and answer
    choices. The user interface is a dynamic shiny app, that displays the images
    and questions and answer choices. The data generated can be saved to a
    file that can be used for subsequent analysis. The original purpose was to
    annotate still images from tennis video for face recognition and emotion 
    detection purposes.

Interface to the 'TA-Lib' (Technical Analysis Library) 'C'
    library, providing access to 150+ indicators (e.g. Average Directional
    Movement Index (ADX), Moving Average Convergence Divergence (MACD),
    Relative Strength Index (RSI), Stochastic Oscillator, Bollinger Bands),
    candlestick pattern recognition, and rolling-window utilities. Core
    computations are implemented in 'C' for fast Open-High-Low-Close-Volume
    (OHLCV) time-series feature engineering and rule-based signal
    generation, with optional interactive visualization via 'plotly'.