Package: experDesign 0.4.0.9000

experDesign: Design Experiments for Batches

Distributes samples in batches while making batches homogeneous according to their description. Allows for an arbitrary number of variables, both numeric and categorical. For quality control it provides functions to subset a representative sample.

Authors:Lluís Revilla Sancho [aut, cre], Juanjo Lozano [ths], Azucena Salas Martinez [ths]

experDesign_0.4.0.9000.tar.gz
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experDesign_0.4.0.9000.tgz(r-4.6-any)experDesign_0.4.0.9000.tgz(r-4.5-any)
experDesign_0.4.0.9000.tar.gz(r-4.7-any)experDesign_0.4.0.9000.tar.gz(r-4.6-any)
experDesign_0.4.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
experDesign/json (API)

# Install 'experDesign' in R:

install.packages('experDesign', repos = c('https://llrs.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/llrs/experdesign/issues

Pkgdown/docs site:https://experdesign.llrs.dev

On CRAN:

batch experiment-design

4.70 score 10 stars 1 scripts 311 downloads 29 exports 0 dependencies

Last updated from:2640afcff8. Checks:2 NOTE, 7 OK. Indexed: yes.

Target	Result	Time
linux-devel	NOTE	139
source / vignettes	OK	256
linux-release	OK	141
macos-release	OK	135
macos-oldrel	OK	200
windows-devel	NOTE	94
windows-release	OK	85
windows-oldrel	OK	102
wasm-release	OK	113

Exports:batch_names check_data check_index compare_index create_subset design distribution entropy evaluate_entropy evaluate_independence evaluate_index evaluate_mad evaluate_mean evaluate_na evaluate_orig evaluate_sd extreme_cases follow_up follow_up2 inspect optimum_batches optimum_subset position_name qcSubset replicates sizes_batches spatial use_index valid_followup

Dependencies:

exprDesign

Rendered fromexperdesign.Rmdusingknitr::rmarkdown

Last update: 2023-03-22
Started: 2018-07-27

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
experDesign: Expert experiment design in batches	experDesign-package experDesign
Name the batch	batch_names
Check experiment data	check_data
Check index distribution on batches	check_index
Compares two indexes	compare_index
Create index of subsets of a data	create_subset
Design a batch experiment	design
Distribution by batch	distribution
Calculates the entropy	entropy
Evaluate entropy	evaluate_entropy
Compare independence by chisq.test	evaluate_independence
Evaluates a data.frame	evaluate_index
Evaluate median absolute deviation	evaluate_mad
Evaluates the mean of the numeric values	evaluate_mean
Evaluate the dispersion of NAs	evaluate_na
Evaluate each variable provided	evaluate_orig
Evaluates the mean of the numeric values	evaluate_sd
Select the subset of extreme cases to evaluation	extreme_cases
Follow up experiments	follow_up
Follow up experiments in batches	follow_up2
Inspect the index	inspect
Optimum values for batches	optimum optimum_batches optimum_subset sizes_batches
Create position names for a grid.	position_name
Random subset	qcSubset
Design a batch experiment with experimental controls	replicates
Distribute the sample on the plate	spatial
Convert a factor to index	use_index
Check the data for a follow up experiment.	valid_followup