Connectome Predictive Modelling (CPM) (Shen et al. (2017)
    <doi:10.1038/nprot.2016.178>) is a method to predict individual
    differences in behaviour from brain functional connectivity. 'cpmr'
    provides a simple yet efficient implementation of this method.

Implementation of conceptual properties norming studies, including estimates of CPNs parameters with their corresponding variances and estimates for the sampling process, and a sampling property function based on a modified empirical distribution from the original data.

Detects multiple changes in slope using the CPOP dynamic programming approach of Fearnhead, Maidstone, and Letchford (2019) <doi:10.1080/10618600.2018.1512868>. This method finds the best continuous piecewise linear fit to data under a criterion that measures fit to data using the residual sum of squares, but penalizes complexity based on an L0 penalty on changes in slope. Further information regarding the use of this package with detailed examples can be found in Fearnhead and Grose (2024) <doi:10.18637/jss.v109.i07>.

Convex Partition is a black-box optimisation algorithm for single
  objective real-parameters functions. The basic principle is to progressively
  estimate and exploit a regression tree similar to a CART (Classification and
  Regression Tree) of the objective function. For more details see 
  'de Paz' (2024) <doi:10.1007/978-3-031-62836-8_3> and
  'Loh' (2011) <doi:10.1002/widm.8> .

CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) <doi:10.1007/978-3-319-11277-0>.

Provides a header only, C++11 interface to R's C
    interface.  Compared to other approaches 'cpp11' strives to be safe
    against long jumps from the C API as well as C++ exceptions, conform
    to normal R function semantics and supports interaction with 'ALTREP'
    vectors.

Provides function declarations and inline function definitions that
    facilitate communication between R and the 'Armadillo' 'C++' library for
    linear algebra and scientific computing. This implementation is detailed
    in Vargas Sepulveda and Schneider Malamud (2024)
    <doi:10.1016/j.softx.2025.102087>.