Provides methods for estimating multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring. Cho, H., Holloway, S. T., and Kosorok, M. R. (2020) <arXiv:2012.03294>.

It is a novel tool used to identify the candidate drugs against a particular disease based on the drug target set enrichment analysis. It assumes the most effective drugs are those with a closer affinity in the protein-protein interaction network to the specified disease. (See Gómez-Carballa et al. (2022) <doi: 10.1016/j.envres.2022.112890> and Feng et al. (2022) <doi: 10.7150/ijms.67815> for disease expression profiles; see Wishart et al. (2018) <doi: 10.1093/nar/gkx1037> and Gaulton et al. (2017) <doi: 10.1093/nar/gkw1074> for drug target information; see Kanehisa et al. (2021) <doi: 10.1093/nar/gkaa970> for the details of KEGG database.)

Basic time series functionalities such as listing of missing
    values, application of arbitrary aggregation as well as rolling (asymmetric)
    window functions and automatic detection of periodicity. As it is mainly
    based on 'data.table', it is fast and (in combination with the 'R6' package)
    offers reference semantics. In addition to its native R6 interface, it
    provides an S3 interface for those who prefer the latter. Finally yet
    importantly, its functional approach allows for incorporating
    functionalities from many other packages.

This package 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>.