doi:10.14569/IJACSA.2017.081266>. The mapping visualization is made available from several features : options in dimension reduction methods and prediction models ranging from linear to non linear regressions. A smoothed version of the map performed using locally weighted regression algorithm is available. A selection process of map stability is provided. A 'shiny' application is included. It presents an easy GUI for the implemented functions as well as a comparative tool of fit models using several criteria. Basic analysis such as characterization of products, panelists and sessions likewise consumer segmentation are also made available.">

SensMap: Sensory and Consumer Data Mapping (original) (raw)

Provides Sensory and Consumer Data mapping and analysis <doi:10.14569/IJACSA.2017.081266>. The mapping visualization is made available from several features : options in dimension reduction methods and prediction models ranging from linear to non linear regressions. A smoothed version of the map performed using locally weighted regression algorithm is available. A selection process of map stability is provided. A 'shiny' application is included. It presents an easy GUI for the implemented functions as well as a comparative tool of fit models using several criteria. Basic analysis such as characterization of products, panelists and sessions likewise consumer segmentation are also made available.

Version: 0.7
Depends: R (≥ 4.0), stats, grDevices, graphics
Imports: doBy, lattice, FactoMineR, MCMCpack, factoextra, fields, ggdendro, glmulti, mgcv, plotly, shiny, ggplot2, reshape2
Suggests: testthat
Published: 2022-07-04
DOI: 10.32614/CRAN.package.SensMap
Author: Ibtihel Rebhi [aut, cre], Dhafer Malouche [ctb]
Maintainer: Ibtihel Rebhi <ibtihel.rebhi at enit.utm.tn>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/IbtihelRebhi/SensMap
NeedsCompilation: no
CRAN checks: SensMap results

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