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albartcoster

pedigree:Pedigree Functions

Pedigree related functions.

Maintained by Albart Coster. Last updated 3 years ago.

cpp

71.1 match 3.88 score 85 scripts 3 dependents

anainesvs

pedigreemm:Pedigree-Based Mixed-Effects Models

Fit pedigree-based mixed-effects models.

Maintained by Ana Ines Vazquez. Last updated 1 years ago.

32.0 match 1 stars 5.42 score 87 scripts 2 dependents

cran

BGLR:Bayesian Generalized Linear Regression

Bayesian Generalized Linear Regression.

Maintained by Paulino Perez Rodriguez. Last updated 5 months ago.

openblas

7.5 match 2 stars 5.18 score 5 dependents

albartcoster

HaploSim:Functions to Simulate Haplotypes

Simulate haplotypes through meioses. Allows specification of population parameters.

Maintained by Albart Coster. Last updated 3 years ago.

5.4 match 3.20 score 12 scripts 4 dependents

jl5000

visged:Visualise GEDCOM Files

Produce a variety of visualisations for family tree GEDCOM files.

Maintained by Jamie Lendrum. Last updated 3 years ago.

3.5 match 2.18 score 3 scripts 1 dependents

cran

BLR:Bayesian Linear Regression

Bayesian Linear Regression.

Maintained by Paulino Perez Rodriguez. Last updated 5 years ago.

3.8 match 2.00 score

yinlilin

hibayes:Individual-Level, Summary-Level and Single-Step Bayesian Regression Model

A user-friendly tool to fit Bayesian regression models. It can fit 3 types of Bayesian models using individual-level, summary-level, and individual plus pedigree-level (single-step) data for both Genomic prediction/selection (GS) and Genome-Wide Association Study (GWAS), it was designed to estimate joint effects and genetic parameters for a complex trait, including: (1) fixed effects and coefficients of covariates, (2) environmental random effects, and its corresponding variance, (3) genetic variance, (4) residual variance, (5) heritability, (6) genomic estimated breeding values (GEBV) for both genotyped and non-genotyped individuals, (7) SNP effect size, (8) phenotype/genetic variance explained (PVE) for single or multiple SNPs, (9) posterior probability of association of the genomic window (WPPA), (10) posterior inclusive probability (PIP). The functions are not limited, we will keep on going in enriching it with more features. References: Meuwissen et al. (2001) <doi:10.1093/genetics/157.4.1819>; Gustavo et al. (2013) <doi:10.1534/genetics.112.143313>; Habier et al. (2011) <doi:10.1186/1471-2105-12-186>; Yi et al. (2008) <doi:10.1534/genetics.107.085589>; Zhou et al. (2013) <doi:10.1371/journal.pgen.1003264>; Moser et al. (2015) <doi:10.1371/journal.pgen.1004969>; Lloyd-Jones et al. (2019) <doi:10.1038/s41467-019-12653-0>; Henderson (1976) <doi:10.2307/2529339>; Fernando et al. (2014) <doi:10.1186/1297-9686-46-50>.

Maintained by Lilin Yin. Last updated 1 years ago.

openblascppopenmp

0.5 match 49 stars 4.43 score 11 scripts

jdench

rSHAPE:Simulated Haploid Asexual Population Evolution

In silico experimental evolution offers a cost-and-time effective means to test evolutionary hypotheses. Existing evolutionary simulation tools focus on simulations in a limited experimental framework, and tend to report on only the results presumed of interest by the tools designer. The R-package for Simulated Haploid Asexual Population Evolution ('rSHAPE') addresses these concerns by implementing a robust simulation framework that outputs complete population demographic and genomic information for in silico evolving communities. Allowing more than 60 parameters to be specified, 'rSHAPE' simulates evolution across discrete time-steps for an evolving community of haploid asexual populations with binary state genomes. These settings are for the current state of 'rSHAPE' and future steps will be to increase the breadth of evolutionary conditions permitted. At present, most effort was placed into permitting varied growth models to be simulated (such as constant size, exponential growth, and logistic growth) as well as various fitness landscape models to reflect the evolutionary landscape (e.g.: Additive, House of Cards - Stuart Kauffman and Simon Levin (1987) <doi:10.1016/S0022-5193(87)80029-2>, NK - Stuart A. Kauffman and Edward D. Weinberger (1989) <doi:10.1016/S0022-5193(89)80019-0>, Rough Mount Fuji - Neidhart, Johannes and Szendro, Ivan G and Krug, Joachim (2014) <doi:10.1534/genetics.114.167668>). This package includes numerous functions though users will only need defineSHAPE(), runSHAPE(), shapeExperiment() and summariseExperiment(). All other functions are called by these main functions and are likely only to be on interest for someone wishing to develop 'rSHAPE'. Simulation results will be stored in files which are exported to the directory referenced by the shape_workDir option (defaults to tempdir() but do change this by passing a folderpath argument for workDir when calling defineSHAPE() if you plan to make use of your results beyond your current session). 'rSHAPE' will generate numerous replicate simulations for your defined range of experimental parameters. The experiment will be built under the experimental working directory (i.e.: referenced by the option shape_workDir set using defineSHAPE() ) where individual replicate simulation results will be stored as well as processed results which I have made in an effort to facilitate analyses by automating collection and processing of the potentially thousands of files which will be created. On that note, 'rSHAPE' implements a robust and flexible framework with highly detailed output at the cost of computational efficiency and potentially requiring significant disk space (generally gigabytes but up to tera-bytes for very large simulation efforts). So, while 'rSHAPE' offers a single framework in which we can simulate evolution and directly compare the impacts of a wide range of parameters, it is not as quick to run as other in silico simulation tools which focus on a single scenario with limited output. There you have it, 'rSHAPE' offers you a less restrictive in silico evolutionary playground than other tools and I hope you enjoy testing your hypotheses.

Maintained by Jonathan Dench. Last updated 6 years ago.

1.6 match 1.00 score