Package: doebioresearch 0.1.0

doebioresearch: Analysis of Design of Experiments for Biological Research

Performs analysis of popular experimental designs used in the field of biological research. The designs covered are completely randomized design, randomized complete block design, factorial completely randomized design, factorial randomized complete block design, split plot design, strip plot design and latin square design. The analysis include analysis of variance, coefficient of determination, normality test of residuals, standard error of mean, standard error of difference and multiple comparison test of means. The package has functions for transformation of data and yield data conversion. Some datasets are also added in order to facilitate examples.

Authors:Raj Popat [aut, cre], Kanthesh Banakara [aut]

doebioresearch_0.1.0.tar.gz
doebioresearch_0.1.0.zip(r-4.7-any)doebioresearch_0.1.0.zip(r-4.6-any)doebioresearch_0.1.0.zip(r-4.5-any)
doebioresearch_0.1.0.tgz(r-4.6-any)doebioresearch_0.1.0.tgz(r-4.5-any)
doebioresearch_0.1.0.tar.gz(r-4.7-any)doebioresearch_0.1.0.tar.gz(r-4.6-any)
doebioresearch_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
doebioresearch/json (API)

# Install 'doebioresearch' in R:
install.packages('doebioresearch', repos = c('https://popatraj.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.32 score 21 scripts 571 downloads 16 exports 6 dependencies

Last updated from:fd14132344. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK108
source / vignettesOK167
linux-releaseOK106
macos-releaseOK128
macos-oldrelOK173
windows-develOK62
windows-releaseOK68
windows-oldrelOK71
wasm-releaseOK96

Exports:arcsineretransformarcsinetransformcrdfcrd2factfcrd3factfrbd2factfrbd3factlogretransformlogtransformlsdrcbdsplitplotsqrtretransformsqrttransformstripplotyieldconvert

Dependencies:agricolaeAlgDesignclusterlatticeMASSnlme