Genetic-Relationship-Matrix Structural Equation Modelling
(GRMSEM)
c(
person("Beate", "StPourcain", email = "Beate.StPourcain@mpi.nl", comment = c(ORCID = "0000-0002-4680-3517"), role = c("aut", "cre")),
person("Alexander", "Klassmann", email = "alex.klassmann@koeln.de", role = c("ctb"))
)
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>.
msm (>= 1.6), numDeriv, optimParallel, stats, utils
Beate StPourcain [aut, cre] (<https://orcid.org/0000-0002-4680-3517>),
Alexander Klassmann [ctb]

The program GRNN implements the algorithm proposed by
Specht (1991).
'create.R' 'grnn-package.r' 'guess.r' 'kernel.R' 'learn.R'
'smooth.R'

grnnR synthesizes a generalized regression neural network
from the supplied training data, P(atterns) and T(argets)

Creating Flexible, Reproducible 'PDF' Reports
c(person(given = "Calvin",
family = "Floyd",
email = "calvin.michael.floyd@gmail.com",
role = c("aut", "cre", "cph")
))
A tool which allows users the ability to intuitively create
flexible, reproducible portable document format reports comprised of
aesthetically pleasing tables, images, plots and/or text.
dplyr, ggplot2, glue, graphics, grDevices, grid, gridExtra,
methods, magrittr, png, purrr, RCurl, stringr, tibble, tools

Generalized Regression on Orthogonal Components
Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models

Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models.