Frequentist sequential meta-analysis based on 
    'Trial Sequential Analysis' (TSA) in programmed in Java by the Copenhagen 
    Trial Unit (CTU). The primary function is the calculation of group 
    sequential designs for meta-analysis to be used for planning and analysis of
    both prospective and retrospective sequential meta-analyses to preserve 
    type-I-error control under sequential testing. 'RTSA' includes tools for 
    sample size and trial size calculation for meta-analysis and core 
    meta-analyses methods such as fixed-effect and random-effects models and
    forest plots. TSA is described in Wetterslev et. al (2008) 
    <doi:10.1016/j.jclinepi.2007.03.013>. The methods for deriving the
    group sequential designs are based on Jennison and Turnbull (1999,
    ISBN:9780849303166).

Adaptation of the 'Matlab' 'tsEVA' toolbox developed by Lorenzo Mentaschi
    available here:
    <https://github.com/menta78/tsEva>. It contains an implementation of the
    Transformed-Stationary (TS) methodology for non-stationary extreme 
    value Analysis (EVA) as described in Mentaschi et al. (2016) <doi:10.5194/hess-20-3527-2016>.  
    In synthesis this approach consists in:
    (i) transforming a non-stationary time series into a
    stationary one to which the stationary extreme value theory can be applied; and
    (ii) reverse-transforming the result into a non-stationary extreme
    value distribution.
    'RtsEva' offers several options for trend estimation (mean, extremes, seasonal)
    and contains multiple plotting functions displaying different aspects
    of the non-stationarity of extremes.

An R wrapper around the fast T-distributed Stochastic
    Neighbor Embedding implementation by Van der Maaten  (see <https://github.com/lvdmaaten/bhtsne/> for more information on the original implementation).

TSON, short for Typed JSON, is a binary-encoded serialization of
    JSON like document that support JavaScript typed data (https://github.com/tercen/TSON).

Performs one-sample t-test based on robustified statistics using median/MAD (TA) and Hodges-Lehmann/Shamos (TB). For more details, see Park and Wang (2018)<arXiv:1807.02215>. This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169).