Tests for a treatment effect using surrogate marker information accounting for heterogeneity in the utility of the surrogate. Details are described in Parast et al (2022) <arXiv:2209.08315>.

Computes diagnostics for linear regression when treatment effects are heterogeneous.
    The output of 'hettreatreg' represents ordinary least squares (OLS) 
    estimates of the effect of a binary treatment as a weighted average of the average treatment effect 
    on the treated (ATT) and the average treatment effect on the untreated (ATU). 
    The program estimates the OLS weights on these parameters, computes the associated model diagnostics, 
    and reports the implicit OLS estimate of the average treatment effect (ATE). 
    See Sloczynski (2019), <http://people.brandeis.edu/~tslocz/Sloczynski_paper_regression.pdf>.

Implements methods developed by Ding, Feller, and Miratrix (2016) <doi:10.1111/rssb.12124> <arXiv:1412.5000>,
    and Ding, Feller, and Miratrix (2018) <doi:10.1080/01621459.2017.1407322> <arXiv:1605.06566>
    for testing whether there is unexplained variation in treatment effects across observations, and for characterizing
    the extent of the explained and unexplained variation in treatment effects. The package includes wrapper functions
    implementing the proposed methods, as well as helper functions for analyzing and visualizing the results of the test.

Structural handling of Finnish identity codes (natural persons and 
    organizations); extract information, check ID validity and diagnostics.

Implements the Clarke-Wright algorithm to find a quasi-optimal solution to the Capacitated Vehicle Routing Problem. See Clarke, G. and Wright, J.R. (1964) <doi:10.1287/opre.12.4.568> for details. The implementation is accompanied by helper functions to inspect its solution.

Implements various heuristics like Take The Best and
    unit-weight linear, which do two-alternative choice: which of
    two objects will have a higher criterion?  Also offers functions
    to assess performance, e.g. percent correct across all row pairs
    in a data set and finding row pairs where models disagree.
    New models can be added by implementing a fit and predict function--
    see vignette.  Take The Best was first described in: Gigerenzer, G.
    & Goldstein, D. G. (1996) <doi:10.1037/0033-295X.103.4.650>.  All
    of these heuristics were run on many data sets and analyzed in:
    Gigerenzer, G., Todd, P. M., & the ABC Group (1999).
    <ISBN:978-0195143812>.