Provides an interactive, virtualized data explorer widget for 'R'.
    Built on 'React' (via 'reactR') and 'htmlwidgets', it offers column-type
    detection, multi-value checkbox filtering, sorting, column visibility
    toggling, virtual scrolling for large datasets, and a full-viewport modal.
    Includes 'dtsmartr_launch()' with an interactive, zero-code file upload wizard
    using 'datamods'. Widgets can be embedded in 'R Markdown' / 'Quarto' documents,
    'Shiny' applications, or exported as standalone HTML files via 'save_dtsmartr()'.

Provides functions for handling missing data using Distributed Trimmed Scores Regression and other imputation methods. It includes facilities for data imputation, evaluation metrics, and clustering analysis. It is designed to work in distributed computing environments to handle large datasets efficiently. The philosophy of the package is described in Guo G. (2024) <doi:10.1080/03610918.2022.2091779>.

Provides functions for 1D and 2D Discrete
        Cosine Transform (DCT), Discrete Sine Transform (DST) and
        Discrete Hartley Transform (DHT).

Manipulates date ('Date'), date time ('POSIXct') and time
    ('hms') vectors.  Date/times are considered discrete and are floored
    whenever encountered.  Times are wrapped and time zones are maintained
    unless explicitly altered by the user.

High-frequency time-series support via 'nanotime' and 'data.table'.

A comprehensive implementation of dynamic time warping
    (DTW) algorithms in R.  DTW computes the optimal (least cumulative
    distance) alignment between points of two time series.  Common DTW
    variants covered include local (slope) and global (window)
    constraints, subsequence matches, arbitrary distance definitions,
    normalizations, minimum variance matching, and so on.  Provides
    cumulative distances, alignments, specialized plot styles, etc.,
    as described in Giorgino (2009) <doi:10.18637/jss.v031.i07>.

Functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), <DOI:10.1016/j.patrec.2017.08.019>. Performance criteria are added to compare similarity between two signals (query and reference).