Showing 11 of total 11 results (show query)
ljacquin
KRMM:Kernel Ridge Mixed Model
Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. <doi:10.3389/fgene.2016.00145>.
Maintained by Laval Jacquin. Last updated 4 months ago.
blupgblupgenomic-predictionkernel-methodsmixed-modelsvariance-components-estimation
11.0 match 1 stars 4.08 score 27 scripts
covaruber
sommer:Solving Mixed Model Equations in R
Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.
Maintained by Giovanny Covarrubias-Pazaran. Last updated 22 days ago.
average-informationmixed-modelsrcpparmadilloopenblascppopenmp
2.5 match 43 stars 12.70 score 300 scripts 9 dependents
psoerensen
qgg:Statistical Tools for Quantitative Genetic Analyses
Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses. Rohde et al. (2019) <doi:10.1101/503631>.
Maintained by Peter Soerensen. Last updated 2 days ago.
3.3 match 36 stars 6.98 score 47 scripts
albartcoster
pedigree:Pedigree Functions
Pedigree related functions.
Maintained by Albart Coster. Last updated 3 years ago.
3.3 match 3.88 score 85 scripts 3 dependents
py-chung
IPLGP:Identification of Parental Lines via Genomic Prediction
Combining genomic prediction with Monte Carlo simulation, three different strategies are implemented to select parental lines for multiple traits in plant breeding. The selection strategies include (i) GEBV-O considers only genomic estimated breeding values (GEBVs) of the candidate individuals; (ii) GD-O considers only genomic diversity (GD) of the candidate individuals; and (iii) GEBV-GD considers both GEBV and GD. The above method can be seen in Chung PY, Liao CT (2020) <doi:10.1371/journal.pone.0243159>. Multi-trait genomic best linear unbiased prediction (MT-GBLUP) model is used to simultaneously estimate GEBVs of the target traits, and then a selection index is adopted to evaluate the composite performance of an individual.
Maintained by Ping-Yuan Chung. Last updated 7 months ago.
4.6 match 1 stars 2.70 scorecovaruber
lme4breeding:Relationship-Based Mixed-Effects Models
Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. The core computational algorithms are implemented using the 'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'.
Maintained by Giovanny Covarrubias-Pazaran. Last updated 22 days ago.
1.3 match 6 stars 5.23 score 7 scripts
ne1s0n
GROAN:Genomic Regression Workbench
Workbench for testing genomic regression accuracy on (optionally noisy) phenotypes.
Maintained by Nelson Nazzicari. Last updated 2 years ago.
1.8 match 3.19 score 31 scripts
tpook92
MoBPS:Modular Breeding Program Simulator
Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. The package is also used as the background simulator for our a web-based interface <http:www.mobps.de>. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.
Maintained by Torsten Pook. Last updated 3 years ago.
2.0 match 2.35 score 45 scripts
pariya
pcgen:Reconstruction of Causal Networks for Data with Random Genetic Effects
Implements the pcgen algorithm, which is a modified version of the standard pc-algorithm, with specific conditional independence tests and modified orientation rules. pcgen extends the approach of Valente et al. (2010) <doi:10.1534/genetics.109.112979> with reconstruction of direct genetic effects.
Maintained by Pariya Behrouzi. Last updated 6 years ago.
2.3 match 1.00 score
cran
predhy.GUI:Genomic Prediction of Hybrid Performance with Graphical User Interface
Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM. GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine. It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).
Maintained by Yuxiang Zhang. Last updated 9 months ago.
0.8 match 1.30 score
yangxu89
predhy:Genomic Prediction of Hybrid Performance
Performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, LightGBM and XGBoost. It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>).
Maintained by Yang Xu. Last updated 10 months ago.
0.5 match 1 stars 1.95 score 1 dependents