Package: SensMap 0.7

Ibtihel Rebhi

SensMap: Sensory and Consumer Data Mapping

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.

Authors:Ibtihel Rebhi [aut, cre], Dhafer Malouche [ctb]

SensMap_0.7.tar.gz
SensMap_0.7.tar.gz(r-4.5-noble)SensMap_0.7.tar.gz(r-4.4-noble)
SensMap_0.7.tgz(r-4.4-emscripten)SensMap_0.7.tgz(r-4.3-emscripten)
SensMap.pdf |SensMap.html
SensMap/json (API)

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

Peer review:

Bug tracker:https://github.com/ibtihelrebhi/sensmap/issues

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT
Datasets:

1.00 score 4 scripts 189 downloads 4 exports 133 dependencies

Last updated 2 years agofrom:20c5221114. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 19 2024
R-4.5-linuxOKNov 19 2024

Exports:PrefMapSensMapUISmoothMapStabMap

Dependencies:abindaskpassbackportsbase64encbootbroombslibcachemcarcarDatacliclustercodacolorspacecommonmarkcorrplotcowplotcpp11crayoncrosstalkcurldata.tabledendextendDerivdigestdoBydotCall64dplyrDTellipseemmeansestimabilityevaluatefactoextraFactoMineRfansifarverfastmapfieldsflashClustfontawesomeFormulafsgenericsggdendroggplot2ggpubrggrepelggsciggsignifglmultigluegridExtragtablehighrhtmltoolshtmlwidgetshttpuvhttrisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslifecyclelme4magrittrmapsMASSMatrixMatrixModelsmcmcMCMCpackmemoisemgcvmicrobenchmarkmimeminqamodelrmultcompViewmunsellmvtnormnlmenloptrnnetnumDerivopensslpbkrtestpillarpkgconfigplotlyplyrpolynompromisespurrrquantregR6rappdirsRColorBrewerRcppRcppEigenreshape2rJavarlangrmarkdownrstatixsassscalesscatterplot3dshinysourcetoolsspamSparseMstringistringrsurvivalsystibbletidyrtidyselecttinytexutf8vctrsviridisviridisLitewithrxfunxtableyaml