Email: URL: http://chris.brien.name

Installing a package from here

While the packages are available on CRAN, they are more frequently updated here so that often a more recent version will be available from here.

I. From this repo(sitory) using drat

  1. Make sure that you have the package drat installed.

  2. When you want to install a package, execute library(drat) followed by addRepo("briencj") in R, if you have not already done so in your current session.

  3. Use install.packages or update.packages in the usual way: e.g. install.packages("asremlPlus").

II. Directly from this repo(sitory) without using drat

Use the R command install.packages(pkgs, repos = "http://briencj.github.io/drat").

Replace install.packages with update.packages to check for updates.

III. Download and install

Use the links on this page to download either the Windows binary, for one or both of the two versions for R for which binaries are available, or the source file for a package, saving them in a directory on your computer. Then use your favourite method for installing packages on your computer. For example, use the R command install.packages(repos=NULL, pkgs="path\\file") where path is the path to the directory where you saved the file and file is the name of the downloaded file.

(Note: To install the source file under Windows, you need to have Rtools installed.)

The packages available

asremlPlus

(last updated 7th April 2025)

The asremlPlus package is a collection of R functions to augment ASReml-R in fitting mixed models and packages generally in exploring prediction differences. The current version is compatible with both ASReml-R versions 3, 4.1 and 4.2, but not 4.0. The current version has known issues when the Intel MKL libraries are installed in the R installation directories.

Versions 4.4 of asremlPlus are compatible with ASReml-R 4.2 and include a number of functions for fitting models for local spatial variation that includes (i) residual correlation models, (ii) two-dimensional tensor-product natural cubic smoothing spline models and (iii) two-dimensional tensor-product P-spline models with the ability to change the degree of the spline and the order of the differencing for the penalty.

Note that most functions are S3 methods and so the object supplied for the first argument must be of the class (the the function name’s suffix) for which the function is a method and the class of the object can be omitted from the function name when calling the function. For example, plotPredictions.data.frame is a plotPredictions method for a data.frame and can be called using just plotPredictions; the object supplied to data must be a data.frame. The alldiffs and data.frame methods in asremlPlus can be applied to objects produced with other mixed modelling software.

For more information, install the package and run the R command news(package = “asremlPlus”). For an overview enter ?asremlPlus. Otherwise, you could consult the manual using vignette("Manual", package = "asremlPlus"). Also available is the Wheat.analysis vignette [vignette("Wheat.analysis", package = "asremlPlus")] that shows how to select the terms, using REML ratio tests, to be included in a mixed model for an experiment that involves spatial variation; it also illustrates diagnostic checking and prediction production and presentation for this example. A second vignette is the Wheat.SpatialModels vignette [vignette("Wheat.SpatialModels", package = "asremlPlus")] that differs from the Wheat.analysis vignette in using the functions for choosing local spatial variation models and in using the AIC to make the choice of model. The third Wheat vignette is the Wheat.infoCriteria vignette [vignette("Wheat.infoCriteria", package = "asremlPlus")] that illustrates the facilities in asremlPlus for producing and using information criteria. Two further vignettes show how to use asremlPlus for exploring and presenting predictions from a linear mixed model analysis in the context of a three-factor factorial experiment on ladybirds: one vignette, Ladybird.asreml vignette [vignette("Ladybird.asreml", package = "asremlPlus")], uses asreml and asremlPlus to produce and present predictions; the other vignette, Ladybird.lm vignette [vignette("Ladybird.lm", package = "asremlPlus")], uses lm to produce the predictions and asremlPlus to present the predictions..

Windows binary R 4.5: asremlPlus_4.4.48.zip; Windows binary R 4.4: asremlPlus_4.4.48.zip; Package source: asremlPlus_4.4.48.tar.gz.

The package is also available from CRAN at https://cran.r-project.org/package=asremlPlus and from the Github repo at https://github.com/briencj/asremlPlus. However, the CRAN version, currently 4.4.48, is not updated as frequently as the version that is here and on GitHub. Older versions of the package and versions for older R versions are available from https://github.com/briencj/drat/tree/gh-pages.

The final version of asremlPlus that was produced specifically for ASReml-R version 3 is version 2.0-13. It is no longer being developed. A version of asremlPlus 2.0-13 built for R 3.5.0 is available as asreml3Plus version 2.0-14; that is, to load this version, a 3 must be included in the package name.

Windows binary R 3.5: asreml3Plus_2.0-14.zip; Package source: asreml3Plus_2.0-14.tar.gz (built under R 3.5.0).

Windows binary R 3.4: asremlPlus_2.0-13.zip; Package source: asremlPlus_2.0-13.tar.gz.

dae

(last updated 2nd December 2024)

The dae package of R functions has been developed to facilitate the use of R for the design and analysis of variance of experiments; these days the emphasis is on design. It is described in the manual, which can be found using vignette("Manual", package = "dae"). Also found using vignette("DesignNotes", package = "dae") is a vignette describing how to use designRandomize to produce randomized layouts for experiments and designAnatomy to assessing the properties of designs. It covers both standard and multiphase experimental designs. The data sets that go with the vignette are available in dae.

Windows binary R 4.5: dae_3.2.30.zip; Windows binary R 4.4: dae_3.2.30.zip; Package source: dae_3.2.30.tar.gz.

The package is also available from CRAN at https://cran.r-project.org/package=dae and from the Github repo at https://github.com/briencj/dae. However, the CRAN version, currently 3.2.30, is not updated as frequently as the version that is here and on GitHub. Older versions of the package and versions for older R versions are available from https://github.com/briencj/drat/tree/gh-pages.

growthPheno

(last updated 1st May 2025)

The growthPheno package is a collection of R functions for the functional analysis of phenotypic growth data to smooth and extract traits (SET), as described by Brien et al. (2020). Version 2.0.15 and subsequent versions represent a major overhaul of the functions and usage of the package. It now has two functions, traitSmooth and traitExtractFeatures, that are sufficient to perform the SET on a set of growth data. In addition, new functions have been added to the package that will eventually replace the corresponding old functions, the new functions having revised arguments as compared to the old functions in an attempt to simplify function calls.

The growthPheno functions are described in growthPheno-manual.pdf, which can be found using vignette("Manual", package = "growthPheno"). An overview can be obtained using ??growthPheno. Two vignettes, Tomato and Rice, illustrate the process for smoothing and extraction of traits (SET), the former being the example presented in Brien et al. (2020). Use vignette("Tomato", package = "growthPheno") or vignette("Rice", package = "growthPheno") to access either of the vignettes. Many of the functions can be applied to longitudinal data in general.

Windows binary R 4.5: growthPheno_3.1.12.zip; Windows binary R 4.4: growthPheno_3.1.12.zip; Package source: growthPheno_3.1.12.tar.gz.

The package is also available from CRAN: https://cran.r-project.org/package=growthPheno and from the Github repo at https://github.com/briencj/growthPheno. However, the CRAN version, currently 3.1.11, is not updated as frequently as the version here. Older versions of the package and versions for older R versions are available from https://github.com/briencj/drat/tree/gh-pages.

Reference

Brien, C., Jewell, N., Garnett, T., Watts-Williams, S. J., & Berger, B. (2020). Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data. Plant Methods, 16, 36. http://dx.doi.org/10.1186/s13007-020-00577-6.

imageData

(last updated 23rd August 2023)

This package has been superseded by growthPheno and is no longer being developed, being retained for legacy purposes only.

The imageData package is a collection of R functions that aids in processing and plotting data from a Lemna-Tec Scananalyzer. It is described in imageData-manual.pdf, which can be found using vignette("Manual", package = "imageData"). An overview can be obtained using ?imageData. The functions can be applied selectively to longitudinal data in general.

Windows binary R 4.3: imageData_0.1-62.zip; Windows binary R 4.2: imageData_0.1-62.zip; Package source: imageData_0.1-62.tar.gz.

The package is also available from CRAN: https://cran.r-project.org/package=imageData.