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1 Type Package Package baystability March 13, 2018 Title Bayesian Stability Analysis of Genotype by Environment Interaction (GEI Version Maintainer Muhammad Yaseen Performs general Bayesian estimation method of linear bilinear models for genotype environment interaction. The method is explained in Perez-Elizalde, S., Jarquin, D., and Crossa, J. (2011 (<doi: /s >. Depends R (>= 3.1 Imports dplyr, ggplot2, ggfortify, lme4, magrittr, MASS, matrixstats, reshape2, rstiefel, rlang, scales, stats, tibble, tidyr, tidyverse License GPL-2 Encoding UTF-8 LazyData true RoxygenNote NeedsCompilation no Author Muhammad Yaseen [aut, cre], Diego Jarquin [aut, ctb], Sergio Perez-Elizalde [aut, ctb], Juan Burgueño [aut, ctb], Jose Crossa [aut, ctb] Repository CRAN Date/Publication :55:34 UTC R topics documented: bayes_ammi cultivo cultivo e_eff
2 2 bayes_ammi ge_ammi ge_eff ge_mean ge_model ge_var g_eff matrix_k orthnorm Index 18 bayes_ammi Bayesian Estimation of Genotype by Environment Interaction Model Bayesian estimation method of linear bilinear models for Genotype by Environment Interaction Model bayes_ammi(.data,.y,.gen,.env,.rep,.niter.data data.frame.y Response Variable.gen Genotypes Factor.env Environment Factor.rep Replication Factor.nIter Number of Iterations Genotype by Environment Interaction Model 2. Diego Jarquin 3. Sergio Perez-Elizalde 4. Juan Burgueño 5. Jose Crossa
3 bayes_ammi 3 data(cultivo2008 fm1 <- ge_ammi(.data = cultivo2008,.rep = rep r0 <- fm1$g c0 <- fm1$e n0 <- fm1$rep k0 <- fm1$k mu0 <- fm1$mu sigma20 <- fm1$sigma2 tau0 <- fm1$tau tao0 <- fm1$tao delta0 <- fm1$delta lambdas0 <- fm1$lambdas alphas0 <- fm1$alphas gammas0 <- fm1$gammas ge_means0 <- fm1$ge_means$ge_means data(cultivo2008 fm2 <- ge_ammi(.data = cultivo2009,.rep = rep k alphasa gammasa alphas1 gammas1 <- fm2$k <- fm2$alphas <- fm2$gammas <- tibble::as_tibble(fm2$alphas <- tibble::as_tibble(fm2$gammas
4 4 bayes_ammi # Biplots OLS library(ggplot2 BiplotOLS1 <- ggplot(data = alphas1, mapping = aes(x = V1, y = V2 + geom_point( + geom_hline(yintercept = 0 + geom_vline(xintercept = 0 + geom_text(aes(label = 1:nrow(alphas1, vjust = "inward", hjust = "inward" + scale_x_continuous( limits = c(-max(abs(c(range(alphas1[, 1:2], max(abs(c(range(alphas1[, 1:2] + scale_y_continuous( limits = c(-max(abs(c(range(alphas1[, 1:2], max(abs(c(range(alphas1[, 1:2] + labs(title = "OLS", x = expression(u[1], y = expression(u[2] + theme_bw( + theme(plot.title = element_text(hjust = 0.5 print(biplotols1 BiplotOLS2 <- ggplot(data = gammas1, mapping = aes(x = V1, y = V2 + geom_point( + geom_hline(yintercept = 0 + geom_vline(xintercept = 0 + geom_text(aes(label = 1:nrow(gammas1, vjust = "inward", hjust = "inward" + scale_x_continuous( limits = c(-max(abs(c(range(gammas1[, 1:2], max(abs(c(range(gammas1[, 1:2] + scale_y_continuous( limits = c(-max(abs(c(range(gammas1[, 1:2], max(abs(c(range(gammas1[, 1:2] + labs(title = "OLS", x = expression(v[1], y = expression(v[2] + theme_bw( + theme(plot.title = element_text(hjust = 0.5 print(biplotols2 BiplotOLS3 <- ggplot(data = alphas1, mapping = aes(x = V1, y = V2 + geom_point( + geom_hline(yintercept = 0 + geom_vline(xintercept = 0 + geom_text(aes(label = 1:nrow(alphas1, vjust = "inward", hjust = "inward" + geom_point(data = gammas1, mapping = aes(x = V1, y = V2 + geom_segment(data = gammas1, aes(x = 0, y = 0, xend = V1, yend = V2, arrow = arrow(length = unit(0.2, "cm", alpha = 0.75, color = "red" + geom_text(data = gammas1, aes(x = V1, y = V2, label = paste0("e", 1:nrow(gammasa, vjust = "inward", hjust = "inward" +
5 bayes_ammi 5 scale_x_continuous( limits = c(-max(abs(c(range(alphas1[, 1:2], gammas1[, 1:2], max(abs(c(range(alphas1[, 1:2], gammas1[, 1:2] + scale_y_continuous( limits = c(-max(abs(c(range(alphas1[, 1:2], gammas1[, 1:2], max(abs(c(range(alphas1[, 1:2], gammas1[, 1:2] + labs(title = "OLS", x = expression(pc[1], y = expression(pc[2] + theme_bw( + theme(plot.title = element_text(hjust = 0.5 print(biplotols3 fm3 <- bayes_ammi(.data = cultivo2009,.rep = rep,.niter = 200 Mean_Alphas <- tibble::as_tibble(matrix(colmeans(fm3$alphas1, ncol = 11 Mean_Gammas <- tibble::as_tibble(matrix(colmeans(fm3$gammas1, ncol = 11 # Biplots Bayesian BiplotBayes1 <- ggplot(data = Mean_Alphas, mapping = aes(x = V1, y = V2 + geom_point( + geom_hline(yintercept = 0 + geom_vline(xintercept = 0 + geom_text(aes(label = 1:nrow(Mean_Alphas, vjust = "inward", hjust = "inward" + scale_x_continuous( limits = c(-max(abs(c(range(mean_alphas[, 1:2], max(abs(c(range(mean_alphas[, 1:2] + scale_y_continuous( limits = c(-max(abs(c(range(mean_alphas[, 1:2], max(abs(c(range(mean_alphas[, 1:2] + labs(title = "Bayes", x = expression(u[1], y = expression(u[2] + theme_bw( + theme(plot.title = element_text(hjust = 0.5 print(biplotbayes1 BiplotBayes2 <- ggplot(data = Mean_Gammas, mapping = aes(x = V1, y = V2 + geom_point( + geom_hline(yintercept = 0 + geom_vline(xintercept = 0 +
6 6 cultivo2008 geom_text(aes(label = 1:nrow(Mean_Gammas, vjust = "inward", hjust = "inward" + scale_x_continuous( limits = c(-max(abs(c(range(mean_gammas[, 1:2], max(abs(c(range(mean_gammas[, 1:2] + scale_y_continuous( limits = c(-max(abs(c(range(mean_gammas[, 1:2], max(abs(c(range(mean_gammas[, 1:2] + labs(title = "Bayes", x = expression(v[1], y = expression(v[2] + theme_bw( + theme(plot.title = element_text(hjust = 0.5 print(biplotbayes2 BiplotBayes3 <- ggplot(data = Mean_Alphas, mapping = aes(x = V1, y = V2 + geom_point( + geom_hline(yintercept = 0 + geom_vline(xintercept = 0 + geom_text(aes(label = 1:nrow(Mean_Alphas, vjust = "inward", hjust = "inward" + geom_point(data = Mean_Gammas, mapping = aes(x = V1, y = V2 + geom_segment(data = Mean_Gammas, aes(x = 0, y = 0, xend = V1, yend = V2, arrow = arrow(length = unit(0.2, "cm", alpha = 0.75, color = "red" + geom_text(data = Mean_Gammas, aes(x = V1, y = V2, label = paste0("e", 1:nrow(Mean_Gammas, vjust = "inward", hjust = "inward" + scale_x_continuous( limits = c(-max(abs(c(range(mean_alphas[, 1:2], Mean_Gammas[, 1:2], max(abs(c(range(mean_alphas[, 1:2], Mean_Gammas[, 1:2] + scale_y_continuous( limits = c(-max(abs(c(range(mean_alphas[, 1:2], Mean_Gammas[, 1:2], max(abs(c(range(mean_alphas[, 1:2], Mean_Gammas[, 1:2] + labs(title = "Bayes", x = expression(pc[1], y = expression(pc[2] + theme_bw( + theme(plot.title = element_text(hjust = 0.5 print(biplotbayes3 cultivo2008 Data for Genotypes by Environment Interaction (GEI cultivo2008 is used for performing Genotypes by Environment Interaction (GEI Analysis.
7 cultivo data(cultivo2008 Format A data.frame 1320 obs. of 6 variables. Details Gen Genotype Institute Institute Rep Replicate Block Block Env Environment Yield Yield Response 2. Diego Jarquin (<diego.jarquin@gmail.com> 3. Sergio Perez-Elizalde (<sergiop@colpos.mx> 4. Juan Burgueño (<j.burgueno@cgiar.org> 5. Jose Crossa (<j.crossa@cgiar.org> data(cultivo2008
8 8 cultivo2009 cultivo2009 Data for Genotypes by Environment Interaction (GEI cultivo2009 is used for performing Genotypes by Environment Interaction (GEI Analysis. data(cultivo2009 Format A data.frame 1320 obs. of 6 variables. Details Gen Genotype Institute Institute Rep Replicate Block Block Env Environment Yield Yield Response 2. Diego Jarquin (<diego.jarquin@gmail.com> 3. Sergio Perez-Elizalde (<sergiop@colpos.mx> 4. Juan Burgueño (<j.burgueno@cgiar.org> 5. Jose Crossa (<j.crossa@cgiar.org> data(cultivo2009
9 e_eff 9 e_eff Environment Effects Calcuates Environment Effects e_eff(.data,.y,.gen,.env.data data.frame.y Response Variable.gen.env Environment Effects Genotypes Factor Environment Factor data(cultivo2008 e_eff(.data = cultivo2008
10 10 ge_ammi ge_ammi AMMI of Genotype by Environment Interaction Model Performs Additive Main Effects and Multiplication Interaction Analysis of Genotype by Environment Interaction Model ge_ammi(.data,.y,.gen,.env,.rep ge_ammi(.data,.y,.gen,.env,.rep.data data.frame.y Response Variable.gen.env.rep Genotypes Factor Environment Factor Replication Factor Genotype by Environment Interaction Model data(cultivo2008 fm1 <- ge_ammi(.data = cultivo2008
11 ge_eff 11,.rep = rep data(cultivo2009 fm2 <- ge_ammi(.data = cultivo2009,.rep = rep ge_eff Genotype by Environment Interaction Effects Calcuates Genotype by Environment Interaction Effects ge_eff(.data,.y,.gen,.env.data data.frame.y Response Variable.gen Genotypes Factor.env Environment Factor Genotype by Environment Interaction Effects
12 12 ge_mean data(cultivo2008 ge_eff(.data = cultivo2008 ge_mean Genotype by Environment Interaction Means Calcuates Genotype by Environment Interaction Means ge_mean(.data,.y,.gen,.env.data data.frame.y Response Variable.gen Genotypes Factor.env Environment Factor Genotype by Environment Interaction Means
13 ge_model 13 data(cultivo2008 ge_mean(.data = cultivo2008 ge_model Genotype by Environment Interaction Model Calcuates Genotype by Environment Interaction Model ge_model(.data,.y,.gen,.env,.rep ge_model(.data,.y,.gen,.env,.rep.data data.frame.y Response Variable.gen.env.rep Genotypes Factor Environment Factor Replication Factor Genotype by Environment Interaction Model
14 14 ge_var data(cultivo2008 fm1 <- ge_model(.data = cultivo2008,.rep = rep ge_var Genotype by Environment Interaction Variances Calcuates Genotype by Environment Interaction Variances ge_var(.data,.y,.gen,.env.data data.frame.y Response Variable.gen.env Genotypes Factor Environment Factor Genotype by Environment Interaction Variances
15 g_eff 15 data(cultivo2008 ge_var(.data = cultivo2008 g_eff Genotype Effects Calcuates Genotype Effects g_eff(.data,.y,.gen,.env.data data.frame.y Response Variable.gen Genotypes Factor.env Environment Factor Genotype Effects
16 16 matrix_k data(cultivo2008 g_eff(.data = cultivo2008 matrix_k k Matrix Gives k matrix matrix_k(n matrix_k(n n Number of columns Matrix
17 orthnorm 17 orthnorm Orthogonal Normalization Perform Orthogonal Normalization of a matrix orthnorm(u = NULL, basis = TRUE, norm = TRUE orthnorm(u = NULL, basis = TRUE, norm = TRUE u basis norm Matrix Logical argument by default TRUE Logical argument by default TRUE Matrix
18 Index Topic datasets cultivo2008, 6 cultivo2009, 8 bayes_ammi, 2 cultivo2008, 6 cultivo2009, 8 e_eff, 9 g_eff, 15 ge_ammi, 10 ge_eff, 11 ge_mean, 12 ge_model, 13 ge_var, 14 matrix_k, 16 orthnorm, 17 18
Package stability. R topics documented: August 23, Type Package
Type Package Package stability August 23, 2018 Title Stability Analysis of Genotype by Environment Interaction (GEI Version 0.3.0 Author Muhammad Yaseen [aut, cre], Kent M. Eskridge [aut, ctb] Maintainer
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Package sbgcop May 29, 2018 Title Semiparametric Bayesian Gaussian Copula Estimation and Imputation Version 0.980 Date 2018-05-25 Author Maintainer Estimation and inference for parameters
Package netdep. July 10, 2018
Type Package Title Testing for Network Dependence Version 0.1.0 Maintainer oujin Lee Imports stats, igraph, igraphdata, MSS, mvrtn Suggests knitr, testthat Package netdep July 10, 2018
Package face. January 19, 2018
Package face January 19, 2018 Title Fast Covariance Estimation for Sparse Functional Data Version 0.1-4 Author Luo Xiao [aut,cre], Cai Li [aut], William Checkley [aut], Ciprian Crainiceanu [aut] Maintainer
Package sindyr. September 10, 2018
Type Package Title Sparse Identification of Nonlinear Dynamics Version 0.2.1 Date 2018-09-10 Author Maintainer Rick Dale Package sindyr September 10, 2018 This implements the Brunton
Package MonoPoly. December 20, 2017
Type Package Title Functions to Fit Monotone Polynomials Version 0.3-9 Date 2017-12-20 Package MonoPoly December 20, 2017 Functions for fitting monotone polynomials to data. Detailed discussion of the
Package clr. December 3, 2018
Type Package Package clr December 3, 2018 Title Curve Linear Regression via Dimension Reduction Version 0.1.0 Author Amandine Pierrot with contributions and/or help from Qiwei Yao, Haeran Cho, Yannig Goude
Package GSODR. March 30, 2018
Type Package Package GSODR March 30, 2018 Title Global Surface Summary of the Day (GSOD) Weather Data from R Version 1.2.0 URL https://github.com/ropensci/gsodr BugReports https://github.com/ropensci/gsodr/issues
Package hurdlr. R topics documented: July 2, Version 0.1
Version 0.1 Package hurdlr July 2, 2017 Title Zero-Inflated and Hurdle Modelling Using Bayesian Inference When considering count data, it is often the case that many more zero counts than would be expected
Package spef. February 26, 2018
Version 1.0-7 Date 2018-02-05 Title Semiparametric Estimating Functions Package spef February 26, 2018 Author Sy Han (Steven) Chiou, Xiaojing Wang and Jun Yan Maintainer Sy Han (Steven) Chiou
Package varband. November 7, 2016
Type Package Package varband November 7, 2016 Title Variable Banding of Large Precision Matrices Version 0.9.0 Implementation of the variable banding procedure for modeling local dependence and estimating
Package dina. April 26, 2017
Type Package Title Bayesian Estimation of DINA Model Version 1.0.2 Date 2017-04-26 Package dina April 26, 2017 Estimate the Deterministic Input, Noisy ``And'' Gate (DINA) cognitive diagnostic model parameters
Package SpatialPack. March 20, 2018
Version 0.3 Date 2018-03-20 Package SpatialPack March 20, 2018 Title Tools for Assessment the Association Between Two Spatial Processes Tools to assess the association between two spatial processes. Currently,
Package cellscape. October 15, 2018
Package cellscape October 15, 2018 Title Explores single cell copy number profiles in the context of a single cell tree Version 1.4.0 Description CellScape facilitates interactive browsing of single cell
Package EnergyOnlineCPM
Type Package Package EnergyOnlineCPM October 2, 2017 Title Distribution Free Multivariate Control Chart Based on Energy Test Version 1.0 Date 2017-09-30 Author Yafei Xu Maintainer Yafei Xu
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Tpe Package Package jaccard June 14, 2018 Title Test Similarit Between Binar Data using Jaccard/Tanimoto Coefficients Version 0.1.0 Date 2018-06-06 Author Neo Christopher Chung , Błażej
Package HMMcopula. December 1, 2018
Type Package Package HMMcopula December 1, 2018 Title Markov Regime Switching Copula Models Estimation and Goodness of Fit Version 1.0.3 Author Mamadou Yamar Thioub , Bouchra
Package noise. July 29, 2016
Type Package Package noise July 29, 2016 Title Estimation of Intrinsic and Extrinsic Noise from Single-Cell Data Version 1.0 Date 2016-07-28 Author Audrey Qiuyan Fu and Lior Pachter Maintainer Audrey Q.
Package leiv. R topics documented: February 20, Version Type Package
Version 2.0-7 Type Package Package leiv February 20, 2015 Title Bivariate Linear Errors-In-Variables Estimation Date 2015-01-11 Maintainer David Leonard Depends R (>= 2.9.0)
Package SMFilter. December 12, 2018
Package SMFilter December 12, 2018 Title Filtering Algorithms for the State Space Models on the Stiefel Manifold Version 1.0.3 Provides the filtering algorithms for the state space models on the Stiefel
Package Delaporte. August 13, 2017
Type Package Package Delaporte August 13, 2017 Title Statistical Functions for the Delaporte Distribution Version 6.1.0 Date 2017-08-13 Description Provides probability mass, distribution, quantile, random-variate
Package MicroMacroMultilevel
Type Package Package MicroMacroMultilevel July 1, 2017 Description Most multilevel methodologies can only model macro-micro multilevel situations in an unbiased way, wherein group-level predictors (e.g.,
Package rnmf. February 20, 2015
Type Package Title Robust Nonnegative Matrix Factorization Package rnmf February 20, 2015 An implementation of robust nonnegative matrix factorization (rnmf). The rnmf algorithm decomposes a nonnegative
Package aspi. R topics documented: September 20, 2016
Type Package Title Analysis of Symmetry of Parasitic Infections Version 0.2.0 Date 2016-09-18 Author Matt Wayland Maintainer Matt Wayland Package aspi September 20, 2016 Tools for the
Package CPE. R topics documented: February 19, 2015
Package CPE February 19, 2015 Title Concordance Probability Estimates in Survival Analysis Version 1.4.4 Depends R (>= 2.10.0),survival,rms Author Qianxing Mo, Mithat Gonen and Glenn Heller Maintainer
Package supernova. February 23, 2018
Type Package Package supernova February 23, 2018 Title Judd & McClelland Formatting for ANOVA Output Version 1.0 Date 2018-02-09 LazyData yes Produces ANOVA tables in the format used by Judd, McClelland,
Package hierdiversity
Version 0.1 Date 2015-03-11 Package hierdiversity March 20, 2015 Title Hierarchical Multiplicative Partitioning of Complex Phenotypes Author Zachary Marion, James Fordyce, and Benjamin Fitzpatrick Maintainer
Package gma. September 19, 2017
Type Package Title Granger Mediation Analysis Version 1.0 Date 2018-08-23 Package gma September 19, 2017 Author Yi Zhao , Xi Luo Maintainer Yi Zhao