Various tools for creating iterators, many patterned after
        functions in the Python itertools module, and others patterned
        after functions in the 'snow' package.

A port of Python's excellent itertools module to R for efficient
    looping.

Functions to clean and process international trade data into an international trade network (ITN) are provided. It then provides a set a functions to undertake analysis and plots of the ITN (extract the backbone, centrality, blockmodels, clustering). Examining the key players in the ITN and regional trade patterns.

The 'Interactive Tree Of Life' <https://itol.embl.de/> online server can 
    edit and annotate trees interactively. The 'itol.toolkit' package can 
    support all types of annotation templates.

Infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, <doi:10.2307/2347233>), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, <doi:10.1101/293993>).

Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies."  Data sets are 'aHDL' from Rosenbaum (2023a) <doi:10.1111/biom.13558> and 'bingeM' from Rosenbaum (2023b) <doi:10.1111/biom.13921>.  The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data 'bingeM' from 'binge'.  The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>).  The book makes use of two other R packages, 'weightedRank' and 'tightenBlock'.

Implements the Interpolate, Truncate, Project (ITP) root-finding 
    algorithm developed by Oliveira and Takahashi (2021) <doi:10.1145/3423597>.
    The user provides the function, from the real numbers to the real numbers, 
    and an interval with the property that the values of the function at its 
    endpoints have different signs. If the function is continuous over this 
    interval then the ITP method estimates the value at which the function is 
    equal to zero. If the function is discontinuous then a point of 
    discontinuity at which the function changes sign may be found. 
    The function can be supplied using either an R function or an external 
    pointer to a C++ function. Tuning parameters of the ITP algorithm can be 
    set by the user. Default values are set based on arguments in Oliveira and 
    Takahashi (2021).