Package: LOGANTree 0.1.1

Qi Qin

LOGANTree: Tree-Based Models for the Analysis of Log Files from Computer-Based Assessments

Enables researchers to model log-file data from computer-based assessments using machine-learning techniques. It allows researchers to generate new knowledge by comparing the performance of three tree-based classification models (i.e., decision trees, random forest, and gradient boosting) to predict student's outcome. It also contains a set of handful functions for the analysis of the features' influence on the modeling. Data from the Climate control item from the 2012 Programme for International Student Assessment (PISA, <https://www.oecd.org/pisa/>) is available for an illustration of the package's capability. He, Q., & von Davier, M. (2015) <doi:10.1007/978-3-319-19977-1_13> Boehmke, B., & Greenwell, B. M. (2019) <doi:10.1201/9780367816377> .

Authors:Denise Reis Costa [aut, ths], Qi Qin [aut, cre]

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LOGANTree.pdf |LOGANTree.html✨
LOGANTree/json (API)

Peer review:

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

On CRAN:

archivedpackagesr-universe

13 exports 5 stars 0.82 score 85 dependencies 217 downloads

Last updated 16 days agofrom:e96b4221c6 (on package/LOGANTree). Checks:OK: 7. Indexed: no.

Exports:ChiSquarePlotChiSquareTableComputeChisquaredDataPartitionDtResultNearZeroVariancePartialDependencePlotPerformanceMetricsRocPlotTreeModelsTreeModelsAllStepsVariableImportancePlotVariableImportanceTable

Dependencies:bitopscaretcaretEnsemblecaToolsclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygbmgenericsggplot2globalsgluegowergplotsgtablegtoolshardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypatchworkpbapplypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangROCRrpartrpart.plotscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr