Package: OTrecod 0.1.2

Gregory Guernec

OTrecod: Data Fusion using Optimal Transportation Theory

In the context of data fusion, the package provides a set of functions dedicated to the solving of 'recoding problems' using optimal transportation theory (Gares, Guernec, Savy (2019) <doi:10.1515/ijb-2018-0106> and Gares, Omer (2020) <doi:10.1080/01621459.2020.1775615>). From two databases with no overlapping part except a subset of shared variables, the functions of the package assist users until obtaining a unique synthetic database, where the missing information is fully completed.

Authors:Gregory Guernec [aut, cre], Valerie Gares [aut], Pierre Navaro [ctb], Jeremy Omer [ctb], Philippe Saint-Pierre [ctb], Nicolas Savy [ctb]

OTrecod_0.1.2.tar.gz
OTrecod_0.1.2.tar.gz(r-4.7-any)OTrecod_0.1.2.tar.gz(r-4.6-any)
OTrecod_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
OTrecod/json (API)

# Install 'OTrecod' in R:
install.packages('OTrecod', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • api29 - Student performance in California schools: the results of the county 29
  • api35 - Student performance in California schools: the results of the county 35
  • ncds_14 - National Child Development Study: a sample of the first four waves of data collection
  • ncds_5 - National Child Development Study: a sample of the fifth wave of data collection
  • simu_data - A simulated dataset to test the functions of the OTrecod package
  • tab_test - A simulated dataset to test the library

On CRAN:

Conda:

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

3.26 score 18 scripts 225 downloads 17 exports 163 dependencies

Last updated from:81f1653d79. Checks:2 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR256
source / vignettesOK300
linux-release-x86_64ERROR231
wasm-releaseOK161

Exports:avg_dist_closestcompare_listserror_grouphamimput_covindiv_grp_closestindiv_grp_optimalmerge_dbsOT_jointOT_outcomepower_setproxim_distselect_predtransfo_disttransfo_qualitransfo_targetverif_OT

Dependencies:abindbackportsbase64encbitbit64bootbroombslibcachemcarcarDatacheckmateclicliprclustercodetoolscoincolorspacecowplotcpp11crayoncrosstalkdata.tableDBIDerivdigestdoBydoParalleldplyrDTellipseemmeansestimabilityevaluateFactoMineRfarverfastmapflashClustfontawesomeforcatsforeachforecastFormulafracdifffsgenericsggplot2ggrepelglmnetgluegtablehavenhighrhmshtmltoolshtmlwidgetsirlbaisobanditeratorsjomojquerylibjsonliteknitrlabelinglaterlatticelazyevalleapslibcoinlifecyclelistcomplme4lmtestlpSolvemagrittrMASSMatrixMatrixModelsmatrixStatsmemoisemgcvmicemicrobenchmarkmimeminqamissMDAmitmlmitoolsmodelrmodeltoolsmultcompmultcompViewmvtnormnlmenloptrnnetnumDerivomprompr.roiordinalotelpanpartypbkrtestpillarpkgconfigplyrprettyunitsprogresspromisesproxypurrrquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenrdistRdpackreadrreformulasregistryRglpkrlangrmarkdownROIROI.plugin.glpkrpartS7sandwichsassscalesscatterplot3dshapeslamSparseMStatMatchstringistringrstrucchangesurveysurvivalTH.datatibbletidyrtidyselecttimeDatetinytextzdbucminfurcautf8vcdvctrsviridisLitevroomwithrxfunyamlzoo

an-application-of-the-OTrecod-package
Package installation | I. The context | II. Harmonization of the data sources | III. Selection of the matching variables | IV. Predicting the missing scales in the databases | A) Transporting target variables to predict the missing scales | B) Transporting target and shared variables to predict the missing scales | V. Validation of the individual predictions | References

Last update: 2022-10-05
Started: 2021-01-29

application-on-real-data-with-na
The NCDS project | The problem | The solution | Package installation | databases installation | Handling missing information | Matching predictors evaluation | Imputation of GO90 using optimal transportation theory | Conclusion

Last update: 2022-10-05
Started: 2022-10-05