LMMsolver - Linear Mixed Model Solver

An efficient and flexible system to solve sparse mixed model equations. Important applications are the use of splines to model spatial or temporal trends as described in Boer (2023). (<doi:10.1177/1471082X231178591>).

Last updated 2 months ago

mixed-modelssplinescpp

8.14 score 11 stars 3 dependents 66 scripts 737 downloads

statgenSTA - Single Trial Analysis (STA) of Field Trials

Phenotypic analysis of field trials using mixed models with and without spatial components. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).

Last updated 5 months ago

geneticssingle-trial-analysis

6.30 score 4 stars 3 dependents 14 scripts 866 downloads

statgenGWAS - Genome Wide Association Studies

Fast single trait Genome Wide Association Studies (GWAS) following the method described in Kang et al. (2010), <doi:10.1038/ng.548>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

Last updated 4 months ago

geneticsgwasopenblascppopenmp

6.08 score 14 stars 3 dependents 15 scripts 1.3k downloads

statgenGxE - Genotype by Environment (GxE) Analysis

Analysis of multi environment data of plant breeding experiments following the analyses described in Malosetti, Ribaut, and van Eeuwijk (2013), <doi:10.3389/fphys.2013.00044>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).

Last updated 6 months ago

geneticsgxegxe-modellingmulti-trial-analysis

5.53 score 10 stars 17 scripts 439 downloads

statgenHTP - High Throughput Phenotyping (HTP) Data Analysis

Phenotypic analysis of data coming from high throughput phenotyping (HTP) platforms, including different types of outlier detection, spatial analysis, and parameter estimation. The package is being developed within the EPPN2020 project (<https://eppn2020.plant-phenotyping.eu/>). Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).

Last updated 3 months ago

geneticshigh-troughput-phenotyping

5.43 score 4 stars 17 scripts 388 downloads

statgenIBD - Calculation of IBD Probabilities

For biparental, three and four-way crosses Identity by Descent (IBD) probabilities can be calculated using Hidden Markov Models and inheritance vectors following Lander and Green (<https://www.jstor.org/stable/29713>) and Huang (<doi:10.1073/pnas.1100465108>). One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

Last updated 1 months ago

geneticshmmibdcpp

5.26 score 1 dependents 8 scripts 700 downloads

douconca - Double Constrained Correspondence Analysis for Trait-Environment Analysis in Ecology

Double constrained correspondence analysis (dc-CA) analyzes (multi-)trait (multi-)environment ecological data by using the 'vegan' package and native R code. Throughout the two step algorithm of ter Braak et al. (2018) is used. This algorithm combines and extends community- (sample-) and species-level analyses, i.e. the usual community weighted means (CWM)-based regression analysis and the species-level analysis of species-niche centroids (SNC)-based regression analysis. The two steps use canonical correspondence analysis to regress the abundance data on to the traits and (weighted) redundancy analysis to regress the CWM of the orthonormalized traits on to the environmental predictors. The function dc_CA() has an option to divide the abundance data of a site by the site total, giving equal site weights. This division has the advantage that the multivariate analysis corresponds with an unweighted (multi-trait) community-level analysis, instead of being weighted. The first step of the algorithm uses vegan::cca(). The second step uses wrda() but vegan::rda() if the site weights are equal. This version has a predict() function. For details see ter Braak et al. 2018 <doi:10.1007/s10651-017-0395-x>.

Last updated 3 months ago

correspondence-analysisecologyecology-modelingmulti-environmentmulti-trait

5.02 score 6 scripts 226 downloads

statgenMPP - QTL Mapping for Multi Parent Populations

For Multi Parent Populations (MPP) Identity By Descend (IBD) probabilities are computed using Hidden Markov Models. These probabilities are then used in a mixed model approach for QTL Mapping as described in Li et al. (<doi:10.1007/s00122-021-03919-7>).

Last updated 30 days ago

geneticsmppqtl-mapping

4.94 score 11 scripts 629 downloads

isatabr - Implementation for the ISA Abstract Model

ISA is a metadata framework to manage an increasingly diverse set of life science, environmental and biomedical experiments. In isatabr methods for reading, modifying and writing of files in the ISA-Tab format are implemented. It also contains methods for processing assay data.

Last updated 2 years ago

isaisatab

3.70 score 2 scripts 248 downloads

statgenQTLxT - Multi-Trait and Multi-Trial Genome Wide Association Studies

Fast multi-trait and multi-trail Genome Wide Association Studies (GWAS) following the method described in Zhou and Stephens. (2014), <doi:10.1038/nmeth.2848>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

Last updated 1 years ago

openblascppopenmp

2.70 score 2 scripts 258 downloads

factReg - Multi-Environment Genomic Prediction with Penalized Factorial Regression

Multi-environment genomic prediction for training and test environments using penalized factorial regression. Predictions are made using genotype-specific environmental sensitivities as in Millet et al. (2019) <doi:10.1038/s41588-019-0414-y>.

Last updated 2 years ago

1.70 score 215 downloads