The ability to tune models is important. 'finetune' enhances
    the 'tune' package by providing more specialized methods for finding
    reasonable values of model tuning parameters.  Two racing methods
    described by Kuhn (2014) <arXiv:1405.6974> are included. An iterative
    search method using generalized simulated annealing (Bohachevsky,
    Johnson and Stein, 1986) <doi:10.1080/00401706.1986.10488128> is also
    included.

Functions to manipulate binary fingerprints
 of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint'
 which is internally represented a vector of integers, such
 that each element represents the position in the fingerprint that is set to 1.
 The bitwise logical functions in R are overridden so that they can be used directly
 with 'fingerprint' objects. A number of distance metrics are also
 available (many contributed by Michael Fadock). Fingerprints 
 can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and
 can also be folded using OR.  Arbitrary fingerprint formats can be handled via line
 handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.

Quantifies the provenance of the sediments in a catchment or study area. Based on a comprehensive characterization of the sediment sources and the end sediment mixtures a mixing model algorithm is applied to the sediment mixtures in order to estimate the relative contribution of each potential source. The package includes several statistical methods such as Kruskal-Wallis test, discriminant function analysis ('DFA'), principal component plot ('PCA') to select the optimal subset of tracer properties. The variability within each sediment source is also considered to estimate the statistical distribution of the sources contribution.

Learning graphs for financial markets with optimization algorithms.
    This package contains implementations of the algorithms described in the paper:
    Cardoso JVM, Ying J, and Palomar DP (2021) <https://papers.nips.cc/paper/2021/hash/a64a034c3cb8eac64eb46ea474902797-Abstract.html>
    "Learning graphs in heavy-tailed markets", Advances in Neural Informations Processing Systems (NeurIPS).