Stress Response score (SRscore) is a stress responsiveness 
    measure for transcriptome datasets and is based on the vote-counting 
    method. The SRscore is determined to evaluate and score genes on the 
    basis of the consistency of the direction of their regulation 
    (Up-regulation, Down-regulation, or No change) under stress conditions 
    across multiple analyzed research projects. This package is based on 
    the HN-score (score based on the ratio of gene expression between
    hypoxic and normoxic conditions) proposed by 
    Tamura and Bono (2022) <doi:10.3390/life12071079>, and can calculate both 
    the original method and an extended calculation method described in 
    Fukuda et al. (2025) <doi:10.1093/plphys/kiaf105>.

Read 'SubRip' <https://sourceforge.net/projects/subrip/>
    subtitle files as data frames for easy text analysis or manipulation.
    Easily shift numeric timings and export subtitles back into valid
    'SubRip' timestamp format to sync subtitles and audio.

Srt file is a common subtitle format for videos, it contains subtitle and when the subtitle showed.
    This package is for align time of srt file, and also change color, style and position of subtitle in videos,
	the srt file will be read as a vector into R, and can be write into srt file after modified using this package.

Use piping, verbs like 'group_by' and 'summarize', and other
    'dplyr' inspired syntactic style when calculating summary statistics on survey
    data using functions from the 'survey' package.

Develops a framework for fisheries stock assessment simulation
    testing with Stock Synthesis (SS) as described in Anderson et al.
    (2014) <doi:10.1371/journal.pone.0092725>.

Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2021); Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).