Package: TestGardener 3.3.3

TestGardener: Information Analysis for Test and Rating Scale Data

Develop, evaluate, and score multiple choice examinations, psychological scales, questionnaires, and similar types of data involving sequences of choices among one or more sets of answers. This version of the package should be considered as brand new. Almost all of the functions have been changed, including their argument list. See the file NEWS.Rd in the Inst folder for more information. Using the package does not require any formal statistical knowledge beyond what would be provided by a first course in statistics in a social science department. There the user would encounter the concept of probability and how it is used to model data and make decisions, and would become familiar with basic mathematical and statistical notation. Most of the output is in graphical form.

Authors:James Ramsay [aut, cre], Juan Li [ctb], Marie Wiberg [ctb], Joakim Wallmark [ctb], Spencer Graves [ctb]

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TestGardener.pdf |TestGardener.html✨
TestGardener/json (API)
NEWS

# Install 'TestGardener' in R:
install.packages('TestGardener', repos = c('https://jamesramsay5.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jamesramsay5/testgardener/issues

Datasets:

On CRAN:

3.48 score 1 stars 3 scripts 419 downloads 32 exports 121 dependencies

Last updated 8 months agofrom:321775349d. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 15 2024
R-4.5-winNOTENov 15 2024
R-4.5-linuxNOTENov 15 2024
R-4.4-winOKNov 15 2024
R-4.4-macOKNov 15 2024
R-4.3-winOKNov 15 2024
R-4.3-macOKNov 15 2024

Exports:Analyzechcemat_simulatedataSimulationdensity_plotDFfunentropiesEntropy_ploteval.surpFcurveFfunFfuns_plotICC_plotindex_distnindex_funindex_searchindex2infomake_dataListmumu_plotPower_plotSbinsmthSbinsmth_nomScope_plotscoreDensityscorePerformanceSensitivity_plotsmooth.surpSpcaSpca_plotTestInfo_svdTG_analysisTG_density.fd

Dependencies:abindashaskpassbackportsbase64encbitopsbootbroombslibcachemcarcarDatacliclustercolorspacecorrplotcowplotcpp11crosstalkcurldata.tableDerivdeSolvedigestdoBydplyrevaluatefansifarverfastmapfdafdsFNNfontawesomeFormulafsgenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtablehdrcdehighrhtmltoolshtmlwidgetshttrisobandjquerylibjsonlitekernlabKernSmoothknitrkslabelinglaterlatticelazyevallifecyclelme4locfitmagrittrMASSMatrixMatrixModelsmclustmemoisemgcvmicrobenchmarkmimeminqamodelrmulticoolmunsellmvtnormnlmenloptrnnetnumDerivopensslpbkrtestpcaPPpillarpkgconfigplotlypolynompracmapromisespurrrquantregR6rainbowrappdirsRColorBrewerRcppRcppEigenRCurlrglrlangrmarkdownrstatixsassscalesSparseMstringistringrsurvivalsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Analyze test or rating scale data defined in 'dataList'.Analyze
Simulate a test or scale data matrix.chcemat_simulate
Simulation Based Estimates of Error Variation of Score Index EstimatesdataSimulation
Plot the probability density function for a set of test scoresdensity_plot
Compute the first and second derivatives of the negative log likelihoodsDFfun
Entropy measures of inter-item dependencyentropies
Plot item entropy curves for selected items or questions.Entropy_plot
Values of a Functional Data Object Defining Surprisal Curves.eval.surp
Construct grid of 101 values of the fitting functionFcurve
Compute the negative log likelihoods associated with a vector of score index values.Ffun
Plot a selection of fit criterion F functions and their first two derivatives.Ffuns_plot
Plotting probability and surprisal curves for an itemICC
Plot probability and surprisal curves for test or scale items.ICC_plot
Compute score densityindex_distn
Compute optimal scoresindex_fun
Ensure that estimated score index is globalindex_search
Compute results using arc length or information as the abscissa.index2info
Make a list object containing information required for analysis of choice data.make_dataList
Compute the expected test score by substituting probability of choices for indicator variable 0-1 values. Binary items assumed coded as two choice items.mu
Plot expected test score as a function of score indexmu_plot
Plot item power curves for selected items or questions.Power_plot
Test data for 24 math calculation questions from the SweSAT data.Quant_13B_problem_chcemat
List of objects essential for an analysis of the abbreviated SweSAT Quantitative multiple choice test.Quant_13B_problem_dataList
Arclength or information parameter list for 24 items from the quantitative SweSAT subtest.Quant_13B_problem_infoList
Option information for the short form of the SweSAT Quantitative test.Quant_13B_problem_key
Parameter list for 24 items from the quantitative SweSAT subtest.Quant_13B_problem_parmList
Estimate the option probability and surprisal curves.Sbinsmth
List vector containing numbers of options and boundaries.Sbinsmth_nom
Plot the score index 'index' as a function of arc length.Scope_plot
Compute and plot a score density histogram and and curve.scoreDensity
Calculate mean squared error and bias for a set of score index values from simulated data.scorePerformance
Plots all the sensitivity curves for selected items or questions.Sensitivity_plot
Simulate Choice Data from a Previous AnalysisSimulateData
Smooth binned probability and surprisal values to make an 'ICC' object.smooth.ICC
Fit data with surprisal smoothing.smooth.surp
Functional principal components analysis of information curveSpca
Plot the test information or scale curve in either two or three dimensions.Spca_plot
Analyses of Tests and Rating Scales using Information or SurprisalTestGardener
Image of the Test Tnformation Curve in 2 or 3 DimensionsTestInfo_svd
Statistics for Multiple choice Tests, Rating Scales and Other Choice Data)TG_analysis
Compute a Probability Density FunctionTG_density.fd