The algorithms are written in C/C++ for faster performance. Note: 'iterpc' is no longer
being maintained. Users are recommended to switch to 'arrangements'.
Imports: iterators, gmp (>= 0.5-12), arrangements (>= 1.0.0)

Description: Various tools for creating iterators, many patterned after
functions in the Python itertools module, and others patterned

Title: itertools2: Functions creating iterators for efficient looping
Author: John A. Ramey <johnramey@gmail.com>,
Description: A port of Python's excellent itertools module to R for efficient

Title: The Interpolate, Truncate, Project (ITP) Root-Finding Algorithm
Description: 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. Tuning
parameters of the ITP algorithm can be set by the user. Default values are
set based on arguments in Oliveira and Takahashi (2021).
Suggests: knitr, rmarkdown, stats, testthat (>= 3.0.0)

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

Description: Parse, trim, join, visualise and analyse data from Itrax sediment core multi-parameter
scanners manufactured by Cox Analytical Systems, Sweden. Functions are provided for parsing
XRF-peak area files, line-scan optical images, and radiographic images, alongside accompanying metadata.
A variety of data wrangling tasks like trimming, joining and reducing XRF-peak area data are simplified.
Principle component analysis (PCA), cluster analysis and associated multivariate methods are
implemented with appropriate data transformation.
Depends: dplyr, ggplot2, ggfortify, grid, R (>= 3.5.0)
Imports: readr, chemometrics, tiff, janitor, ggcorrplot, rlang,