Showing 27 of total 27 results (show query)
gaynorr
AlphaSimR:Breeding Program Simulations
The successor to the 'AlphaSim' software for breeding program simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].
Maintained by Chris Gaynor. Last updated 5 months ago.
breedinggenomicssimulationopenblascppopenmp
30.0 match 47 stars 10.22 score 534 scripts 2 dependents
wviechtb
metafor:Meta-Analysis Package for R
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) <doi:10.18637/jss.v036.i03>.
Maintained by Wolfgang Viechtbauer. Last updated 2 days ago.
meta-analysismixed-effectsmultilevel-modelsmultivariate
5.3 match 246 stars 16.30 score 4.9k scripts 92 dependents
guido-s
meta:General Package for Meta-Analysis
User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker <DOI:10.1007/978-3-319-21416-0>, "Meta-Analysis with R" (2015): - common effect and random effects meta-analysis; - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble); - three-level meta-analysis model; - generalised linear mixed model; - logistic regression with penalised likelihood for rare events; - Hartung-Knapp method for random effects model; - Kenward-Roger method for random effects model; - prediction interval; - statistical tests for funnel plot asymmetry; - trim-and-fill method to evaluate bias in meta-analysis; - meta-regression; - cumulative meta-analysis and leave-one-out meta-analysis; - import data from 'RevMan 5'; - produce forest plot summarising several (subgroup) meta-analyses.
Maintained by Guido Schwarzer. Last updated 26 days ago.
3.3 match 84 stars 14.84 score 2.3k scripts 29 dependents
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
11.9 match 1 stars 4.08 score 27 scripts
jendelman
rrBLUP:Ridge Regression and Other Kernels for Genomic Selection
Software for genomic prediction with the RR-BLUP mixed model (Endelman 2011, <doi:10.3835/plantgenome2011.08.0024>). One application is to estimate marker effects by ridge regression; alternatively, BLUPs can be calculated based on an additive relationship matrix or a Gaussian kernel.
Maintained by Jeffrey Endelman. Last updated 1 years ago.
7.1 match 13 stars 6.60 score 568 scripts 7 dependents
gasparrini
mvmeta:Multivariate and Univariate Meta-Analysis and Meta-Regression
Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.
Maintained by Antonio Gasparrini. Last updated 5 years ago.
5.6 match 6 stars 7.29 score 151 scripts 10 dependentsgasparrini
mixmeta:An Extended Mixed-Effects Framework for Meta-Analysis
A collection of functions to perform various meta-analytical models through a unified mixed-effects framework, including standard univariate fixed and random-effects meta-analysis and meta-regression, and non-standard extensions such as multivariate, multilevel, longitudinal, and dose-response models.
Maintained by Antonio Gasparrini. Last updated 3 years ago.
5.6 match 13 stars 6.96 score 63 scripts 13 dependents
biozhp
Phenotype:A Tool for Phenotypic Data Processing
Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The 'Phenotype' can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) <doi:10.1007/s10681-007-9449-8>.
Maintained by Peng Zhao. Last updated 5 years ago.
7.4 match 1 stars 3.70 score
kbroman
regress:Gaussian Linear Models with Linear Covariance Structure
Functions to fit Gaussian linear model by maximising the residual log likelihood where the covariance structure can be written as a linear combination of known matrices. Can be used for multivariate models and random effects models. Easy straight forward manner to specify random effects models, including random interactions. Code now optimised to use Sherman Morrison Woodbury identities for matrix inversion in random effects models. We've added the ability to fit models using any kernel as well as a function to return the mean and covariance of random effects conditional on the data (best linear unbiased predictors, BLUPs). Clifford and McCullagh (2006) <https://www.r-project.org/doc/Rnews/Rnews_2006-2.pdf>.
Maintained by Karl W Broman. Last updated 2 years ago.
3.8 match 4 stars 5.94 score 146 scripts 1 dependents
rqtl
qtl2:Quantitative Trait Locus Mapping in Experimental Crosses
Provides a set of tools to perform quantitative trait locus (QTL) analysis in experimental crosses. It is a reimplementation of the 'R/qtl' package to better handle high-dimensional data and complex cross designs. Broman et al. (2019) <doi:10.1534/genetics.118.301595>.
Maintained by Karl W Broman. Last updated 9 days ago.
1.7 match 34 stars 9.48 score 1.1k scripts 5 dependents
thlytras
FluMoDL:Influenza-Attributable Mortality with Distributed-Lag Models
Functions to estimate the mortality attributable to influenza and temperature, using distributed-lag nonlinear models (DLNMs), as first implemented in Lytras et al. (2019) <doi:10.2807/1560-7917.ES.2019.24.14.1800118>. Full descriptions of underlying DLNM methodology in Gasparrini et al. <doi:10.1002/sim.3940> (DLNMs), <doi:10.1186/1471-2288-14-55> (attributable risk from DLNMs) and <doi:10.1002/sim.5471> (multivariate meta-analysis).
Maintained by Theodore Lytras. Last updated 6 years ago.
4.8 match 1 stars 3.32 score 42 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
1.1 match 43 stars 12.70 score 300 scripts 9 dependents
marcoolopez
SFSI:Sparse Family and Selection Index
Here we provide tools for the estimation of coefficients in penalized regressions when the (co)variance matrix of predictors and the covariance vector between predictors and response, are provided. These methods are extended to the context of a Selection Index (commonly used for breeding value prediction). The approaches offer opportunities such as the integration of high-throughput traits in genetic evaluations ('Lopez-Cruz et al., 2020') <doi:10.1038/s41598-020-65011-2> and solutions for training set optimization in Genomic Prediction ('Lopez-Cruz & de los Campos, 2021') <doi:10.1093/genetics/iyab030>.
Maintained by Marco Lopez-Cruz. Last updated 7 months ago.
3.0 match 4.60 score 20 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
alenxav
SoyNAM:Soybean Nested Association Mapping Dataset
Genomic and multi-environmental soybean data. Soybean Nested Association Mapping (SoyNAM) project dataset funded by the United Soybean Board (USB). BLUP function formats data for genome-wide prediction and association analysis.
Maintained by Alencar Xavier. Last updated 3 years ago.
3.8 match 1 stars 3.18 score 4 scripts
flavjack
inti:Tools and Statistical Procedures in Plant Science
The 'inti' package is part of the 'inkaverse' project for developing different procedures and tools used in plant science and experimental designs. The mean aim of the package is to support researchers during the planning of experiments and data collection (tarpuy()), data analysis and graphics (yupana()) , and technical writing. Learn more about the 'inkaverse' project at <https://inkaverse.com/>.
Maintained by Flavio Lozano-Isla. Last updated 2 days ago.
agricultureappsinkaverselmmplant-breedingplant-scienceshiny
1.2 match 5 stars 8.27 score 193 scripts
biometris
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/>).
Maintained by Bart-Jan van Rossum. Last updated 6 months ago.
geneticsgxegxe-modellingmulti-trial-analysis
1.8 match 10 stars 5.53 score 17 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.
2.9 match 3.19 score 31 scripts
drj001
wgaim:Whole Genome Average Interval Mapping for QTL Detection and Estimation using ASReml-R
A computationally efficient whole genome approach to detecting and estimating significant QTL in linkage maps using the flexible linear mixed modelling functionality of ASReml-R.
Maintained by Julian Taylor. Last updated 7 months ago.
1.6 match 2 stars 5.20 score 16 scriptsalenxav
NAM:Nested Association Mapping
Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.
Maintained by Alencar Xavier. Last updated 5 years ago.
1.3 match 2 stars 5.72 score 44 scripts 1 dependents
sgezan
ASRgenomics:Complementary Genomic Functions
Presents a series of molecular and genetic routines in the R environment with the aim of assisting in analytical pipelines before and after the use of 'asreml' or another library to perform analyses such as Genomic Selection or Genome-Wide Association Analyses. Methods and examples are described in Gezan, Oliveira, Galli, and Murray (2022) <https://asreml.kb.vsni.co.uk/wp-content/uploads/sites/3/ASRgenomics_Manual.pdf>.
Maintained by Salvador Gezan. Last updated 1 years ago.
1.8 match 1 stars 2.28 score 38 scripts
laportefab
MM4LMM:Inference of Linear Mixed Models Through MM Algorithm
The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Laporte, F., Charcosset, A. & Mary-Huard, T. (2022) <doi:10.1371/journal.pcbi.1009659>).
Maintained by Fabien Laporte. Last updated 5 months ago.
2.0 match 1.82 score 11 scripts 2 dependents
cran
onlinePCA:Online Principal Component Analysis
Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.
Maintained by David Degras. Last updated 1 years ago.
1.9 match 2 stars 1.30 score
