Quantitative genetics tool supporting the modelling of multivariate 
 genetic variance structures in quantitative data. It allows fitting different 
 models through multivariate genetic-relationship-matrix (GRM) 
 structural equation modelling (SEM) in unrelated individuals, 
 using a maximum likelihood approach. Specifically, 
 it combines genome-wide genotyping information, as captured by GRMs, 
 with twin-research-based SEM techniques, 
 St Pourcain et al. (2017) <doi:10.1016/j.biopsych.2017.09.020>, 
 Shapland et al. (2020) <doi:10.1101/2020.08.14.251199>.

Provides methods for recursive partitioning based on the 
  'Graded Response Model' ('GRM'), extending the 'MOB' algorithm from the 
  'partykit' package. The package allows for fitting
  'GRM' trees that partition the population into homogeneous 
  subgroups based on item response patterns and covariates. 
  Includes specialized plotting functions for visualizing 'GRM' trees
  with different terminal node displays (threshold regions, 
  parameter profiles, and factor score distributions). The package also implements the Longitudinal GRMTree for detecting response shift in PROMs measured at two time points, embedding a constrained two-factor longitudinal GRM within recursive partitioning, with post-hoc characterization of recalibration and reprioritization.
  For more details on the methods, see Samejima (1969) <doi:10.1002/J.2333-8504.1968.TB00153.X>, Komboz et al. (2018) <doi:10.1177/0013164416664394> and Arimoro et al. (2025) <doi:10.1007/s11136-025-04018-6>.

The program GRNN implements the algorithm proposed by
        Specht (1991).

This General Regression Neural Networks Package uses various distance functions.
     It was motivated by Specht (1991, ISBN:1045-9227), and updated from previous published paper 
     Li et al. (2016) <doi:10.1016/j.palaeo.2015.11.005>. This package includes various functions, 
     although "euclidean" distance is used traditionally.

Workbench for testing genomic regression accuracy on
    (optionally noisy) phenotypes.