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ConsensusOPLS
View on CRAN: Click
here
Download and install ConsensusOPLS package within the R console
Install from CRAN:
install.packages("ConsensusOPLS")
Install from Github:
library("remotes")
install_github("cran/ConsensusOPLS")
Install by package version:
library("remotes")
install_version("ConsensusOPLS", "1.1.0")
Attach the package and use:
library("ConsensusOPLS")
Maintained by
Van Du T. Tran
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.ConsensusOPLS . ConsensusOPLS results . ConsensusOPLS.pdf . ConsensusOPLS_1.1.0.tar.gz . ConsensusOPLS_1.1.0.zip . ConsensusOPLS_1.1.0.zip . ConsensusOPLS_1.1.0.zip . ConsensusOPLS_1.1.0.tgz . ConsensusOPLS_1.1.0.tgz . ConsensusOPLS_1.1.0.tgz . ConsensusOPLS_1.1.0.tgz . ConsensusOPLS_1.1.0.tgz . ConsensusOPLS_1.1.0.tgz . ConsensusOPLS archive . https://CRAN.R-project.org/package=ConsensusOPLS .
First Published: 2024-06-20
Latest Update: 2024-06-20
Description:
Merging data from multiple sources is a relevant approach for comprehensively evaluating complex systems. However, the inherent problems encountered when analyzing single tables are amplified with the generation of multi-block datasets, and finding the relationships between data layers of increasing complexity constitutes a challenging task. For that purpose, a generic methodology is proposed by combining the strengths of established data analysis strategies, i.e. multi-block approaches and the Orthogonal Partial Least Squares (OPLS) framework to provide an efficient tool for the fusion of data obtained from multiple sources. The package enables quick and efficient implementation of the consensus OPLS model for any horizontal multi-block data structure (observation-based matching). Moreover, it offers an interesting range of metrics and graphics to help to determine the optimal number of components and check the validity of the model through permutation tests. Interpretation tools include scores and loadings plots, as well as Variable Importance in Projection (VIP), and performance coefficients such as R2, Q2 and DQ2 coefficients. J. Boccard and D.N. Rutledge (2013) <doi:10.1016/j.aca.2013.01.022>.
How to cite:
Van Du T. Tran (2024). ConsensusOPLS: Consensus OPLS for Multi-Block Data Fusion. R package version 1.1.0, https://cran.r-project.org/web/packages/ConsensusOPLS. Accessed 02 Apr. 2025.
Previous versions and publish date:
1.0.0 (2024-06-20 18:00)
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