Package: SCCDdesign 0.2.0

Vyshna I C

SCCDdesign: Construction of Screening Designs for Mixed Level Continuous and Categorical Factors

Constructs screening designs for experiments involving continuous and categorical factors with multiple levels. The package implements methods for constructing mixed-level screening designs, involving factors with more than two levels. It also evaluates the statistical performance of screening designs throughdev power to identify active effects and Type I error rates. The package implements three methods proposed by Jones, B., Lekivetz, R., Majumdar, D. and Nachtsheim, C. (2025) <doi:10.1080/00401706.2024.2362149> for generating efficient screening designs involving three-level continuous and two-level categorical factors for even run sizes. It also includes Paley Type I and Type II constructions for conference matrices and pseudo conference matrices obtained using the coordinate exchange algorithm by Jones, B. and Nachtsheim, C. J. (2011) <doi:10.1080/00224065.2011.11917841> which are used in the development of these screening designs.

Authors:Vyshna I C [aut, cre], Cini Varghese [aut, ctb], Safeela Nasrin [aut], Boyina Devi Priyanka [aut, ctb], Mohd Harun [aut, ctb], Anindita Datta [aut, ctb]

SCCDdesign_0.2.0.tar.gz
SCCDdesign_0.2.0.zip(r-4.7-any)SCCDdesign_0.2.0.zip(r-4.6-any)
SCCDdesign_0.2.0.tar.gz(r-4.7-any)SCCDdesign_0.2.0.tar.gz(r-4.6-any)
SCCDdesign_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
SCCDdesign/json (API)

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

On CRAN:

Conda:

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

1.30 score 168 downloads 7 exports 3 dependencies

Last updated from:118de55a16. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK166
source / vignettesOK198
linux-releaseOK152
windows-develOK58
windows-releaseOK72
wasm-releaseOK103

Exports:conferenceMatrixdOptimalTwoLevelhadamardMatrixmixed_level_designpseudoConferenceSCCDsimulation

Dependencies:AlgDesignleapspracma

Readme and manuals

Help Manual

Help pageTopics
Constructs a conference matrix of a specified order using a direct construction for order 2 and Paley-based constructions for supported odd prime or prime-power values of q = n - 1.conferenceMatrix
Constructs a D-optimal two-level design with 'm' runs and 'm - 1' factors. The factor levels are coded as -1 and +1.dOptimalTwoLevel
Constructs a Hadamard matrix of order 'n' using 'pracma::hadamard()'.hadamardMatrix
Constructs an orthogonal design for two types of factors with different numbers of levels. Three construction cases are considered: even-even level factors, odd-even level factors and odd-odd level factors. The appropriate construction is selected automatically according to the parity of the numbers of levels of the two factors. Hadamard matrices are used for even-level factors, while conference matrices are used for odd-level factors. The order of both the Hadamard and conference matrices is 'm'. For the even-even case, the design contains 'm - 1' factors of each type. For the odd-even case, the design contains 'm' odd-level factors and 'm - 1' even-level factors. For the odd-odd case, the design contains 'm' factors of each type.mixed_level_design
Constructs a pseudo-conference matrix of order 'n' using a determinant-based search procedure.pseudoConference
Constructs an SCCD for a specified number of runs (n=2m). The construction method is selected automatically according to the number of runs.SCCD
Performs a simulation study to evaluate the ability of a design to identify active main effects using best-subsets regression and the AICc criterion.simulation