Functions to compute and plot tracheidograms, as in De Soto et al. (2011) <doi:10.1139/x11-045>.

Rapid advancements in high-throughput gene sequencing
    technologies have resulted in genome-scale time-series datasets. 
    Uncovering the underlying temporal sequence of gene regulatory events 
    in the form of time-varying gene regulatory networks demands 
    accurate and computationally efficient algorithms. Such an
    algorithm is 'TGS'. It is proposed in Saptarshi Pyne, Alok Ranjan 
    Kumar, and Ashish Anand. Rapid reconstruction of time-varying 
    gene regulatory networks. IEEE/ACM Transactions on Computational 
    Biology and Bioinformatics, 17(1):278{291, Jan-Feb 2020. The TGS 
    algorithm is shown to consume only 29 minutes for a microarray 
    dataset with 4028 genes. This package provides an implementation 
    of the TGS algorithm and its variants.

Functions for implementing the targeted gold standard (GS) testing. You provide the true disease or treatment failure status and the risk score, tell 'TGST' the availability of GS tests and which method to use, and it returns the optimal tripartite rules. Please refer to Liu et al. (2013) <doi:10.1080/01621459.2013.810149> for more details.

A collection of high performance utilities to compute
    distance, correlation, auto correlation, clustering and other tasks.
    Contains graph clustering algorithm described in "MetaCell: analysis
    of single-cell RNA-seq data using K-nn graph partitions" (Yael Baran,
    Akhiad Bercovich, Arnau Sebe-Pedros, Yaniv Lubling, Amir Giladi, Elad
    Chomsky, Zohar Meir, Michael Hoichman, Aviezer Lifshitz & Amos Tanay,
    2019 <doi:10.1186/s13059-019-1812-2>).

Turing Geovisualization Engine R package for geospatial visualization and analysis.

Contains data sets used in other packages Torsten Hothorn
  maintains.

Provide R Markdown templates and LaTeX preamble
    which are necessary for creating PDF from R Markdown documents in Thai language.

Implements the truncated harmonic mean estimator (THAMES) 
    of the reciprocal marginal likelihood using posterior samples and
    unnormalized log posterior values via reciprocal importance sampling.
    Metodiev, Perrot-Dockès, Ouadah, Irons, & Raftery (2023) <arXiv:2305.08952>.