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growthPheno  

Functional Analysis of Phenotypic Growth Data to Smooth and Extract Traits
View on CRAN: Click here


Download and install growthPheno package within the R console
Install from CRAN:
install.packages("growthPheno")

Install from Github:
library("remotes")
install_github("cran/growthPheno")

Install by package version:
library("remotes")
install_version("growthPheno", "2.1.24")



Attach the package and use:
library("growthPheno")
Maintained by
Chris Brien
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-05-14
Latest Update: 2024-02-29
Description:
Assists in the plotting and functional smoothing of traits measured over time and the extraction of features from these traits, implementing the SET (Smoothing and Extraction of Traits) method described in Brien et al. (2020) Plant Methods, 16. Smoothing of growth trends for individual plants using natural cubic smoothing splines or P-splines is available for removing transient effects and segmented smoothing is available to deal with discontinuities in growth trends. There are graphical tools for assessing the adequacy of trait smoothing, both when using this and other packages, such as those that fit nonlinear growth models. A range of per-unit (plant, pot, plot) growth traits or features can be extracted from the data, including single time points, interval growth rates and other growth statistics, such as maximum growth or days to maximum growth. The package also has tools adapted to inputting data from high-throughput phenotyping facilities, such from a Lemna-Tec Scananalyzer 3D (see for more information). The package 'growthPheno' can also be installed from .
How to cite:
Chris Brien (2019). growthPheno: Functional Analysis of Phenotypic Growth Data to Smooth and Extract Traits. R package version 2.1.24, https://cran.r-project.org/web/packages/growthPheno
Previous versions and publish date:
1.0-13 (2019-05-14 16:20), 1.0-15 (2019-07-15 10:10), 1.0-21 (2020-01-12 15:40), 1.0-22 (2020-03-18 07:10), 1.0-26 (2020-07-09 15:00), 1.0-30 (2020-12-12 09:20), 1.0-34 (2021-10-14 12:30), 2.1.16 (2022-11-15 19:30), 2.1.17 (2023-01-13 23:40), 2.1.19 (2023-03-15 06:00), 2.1.21 (2023-08-22 18:00), 2.1.23 (2023-10-24 15:40)
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