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nadiv

R package that constructs (non)additive genetic relationship matrices, and their inverses, from a pedigree to be used in linear mixed effect models (A.K.A. the 'animal model'). Also includes other functions to facilitate the use of animal models. Some functions have been created to be used in conjunction with the R package for ASReml software.

See the latest developments:

nadiv NEWS page

Overview of main branches:

master branch is the most recent production version (typically the same as what is available from the R CRAN mirrors)
devel branch is a preview of the next release which should be functional and error/bug free, but proceed with caution

To obtain nadiv:

From R:
- see the package page for the latest release of nadiv on CRAN where you can download the source.
- install the latest release of the package directly in R:

   install.packages("nadiv")

- then select your favorite [CRAN mirror](https://CRAN.R-project.org/)

From GitHub:
- clone or download the latest development version here
- install the latest development version directly in R using the remotes package https://github.com/r-lib/remotes:

   library(remotes); install_github("matthewwolak/nadiv", ref = "devel")

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Version

Install

install.packages('nadiv')

Monthly Downloads

641

Version

2.17.2

License

GPL (>= 2)

Issues

Pull Requests

Stars

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Repository

https://github.com/matthewwolak/nadiv

Maintainer

Matthew Wolak

Last Published

December 7th, 2022

Functions in nadiv (2.17.2)

Pedigree, adapted from example 9.1 of Mrode (2005)

log-Likelihood Ratio Test

Sampling (co)variances

Confidence Intervals for Variance Components

Pedigree, adapted from Wray (1990)

Pedigree with genetic groups adapted from Quaas (1988) equation [5]

Pedigree, from chapter 3 of Mrode (2005) with genetic groups and a trait column

Pedigree from Table 2.1 of Mrode (2005)

Simulated dataset used to analyze data with genetic group animal models

Finds the double first cousins in a pedigree

Identifies the matriline or patriline to which each individual in a pedigree belongs

Generation assignment

Function used in conjunction with others to produce a profile likelihood for a variance component

Functions to conduct gene dropping through a pedigree

Akaike Information Criterion

Simulated design random effects

Genetic group contribution

Simulated genetic random effects

Creates the inverse additive genetic relationship matrix with genetic groups

Create the inverse (additive) mutational effects relationship matrix

Creates the inverse additive genetic relationship matrix

Creates the additive genetic relationship matrix for the shared sex chromosomes

Creates the additive by dominance and dominance by dominance epistatic genetic relationship matrices

Create the dominance genetic relationship matrix

Prepares a pedigree by sorting and adding 'founders'

Creates the additive by additive epistatic genetic relationship matrix

Approximate standard errors for linear functions of variance components

Creates the additive genetic relationship matrix

(Non)Additive Genetic Relatedness Matrices in Animal Model Analyses

Create the dominance genetic relationship matrix through an iterative (simulation) process

Creates the (additive) mutational effects relationship matrix

parConstrainFun

Function used in the proLik function to produce a profile likelihood for a variance component

REML convergence checks

Middle Class Neighborhood pedigree construction

Double first cousin pedigree construction

Half-sib pedigree construction

Genetic group pedigree and data simulation

Prunes a pedigree based on individuals with phenotypes

Profile Likelihoods

Pedigree and phenotypic values for a mythical population of Warcolaks

Integer Format Pedigree

nadiv-deprecated

Deprecated functions in package nadiv.

Creates components of the additive genetic relationship matrix and its inverse

Transforms ASReml-R gamma sampling variances to component scale

Converts a sparse matrix into a three column format.

Pedigree, adapted from Table 1 in Fernando & Grossman (1990)

Pedigree adapted from Fikse 2009 with genetic groups and fuzzy classification