Implementation of the empirical method to derive log2 counts per million (CPM) cutoff to filter out lowly expressed genes using ERCC spike-ins as described in Goll and Bosinger et.al (2022)<doi:10.1101/2022.06.23.497396>. This package utilizes the synthetic mRNA control pairs developed by the External RNA Controls Consortium (ERCC) (ERCC 1 / ERCC 2) that are spiked into sample pairs at known ratios at various absolute abundances.  The relationship between the observed and expected fold changes is then used to empirically determine an optimal log2 CPM cutoff for filtering out lowly expressed genes.

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> .