|
AdjustFormatTable()
|
Adjust Format Table |
|
NP.ApproximateConfidenceInterval()
|
Approximate Non-Parametric Confidence Interval |
|
PlotConfidenceCurve()
|
Plot Confidence Curve
Plot confidence intervals for all levels of significance. |
|
ad.test()
|
Anderson-Darling test for goodness of fit |
|
attParse()
|
Parse XML attributes to a data frame |
|
bayesian.imprecise()
|
Bayesian Imprecise Dirichlet Prior |
|
bayesianCorrelatedT.test()
|
Bayesian Correlayed t-test |
|
bayesianFriedman.test()
|
Bayesian Friedman Test |
|
bayesianMultipleConditions.test()
|
Bayesian Test for Multiple Performance Measures Comparison |
|
bayesianSign.test()
|
Bayesian Sign test |
|
bayesianSignedRank.test()
|
Bayesian Signed-Rank test |
|
binomialSign.test()
|
Sign test for one sample |
|
cd.test()
|
Chakraborti and Desu test for equality |
|
cec17.extended
|
Results of executions of CEC'17 algorithms |
|
cec17.extended.final
|
Final results of CEC'17 algorithms |
|
cec17.final
|
Final mean results of CEC'17 algorithms |
|
cec17.mean
|
Mean of results between executions of CEC'17 algorithms |
|
checkBivariateConditions()
|
Checks Conditions |
|
checkCountDataConditions()
|
Checks Conditions |
|
checkMultipleMeasuresConditions()
|
Check Multiple Measures Conditions |
|
chiSquare.test()
|
Chi square test for goodness of fit |
|
computeAproximatedProbability()
|
Compute exact probability of distribution |
|
computeCDAsymtoticProbability()
|
Chakraborti and Desu Asymtotic Probability |
|
computeCDExactProbability()
|
Chakraborti and Desu Exact Probability |
|
computeExactProbability()
|
Compute exact probability of distribution |
|
computeFisherLeftExactProbability()
|
Compute Fisher Left Exact Probability |
|
computeFisherRightExactProbability()
|
Compute Fisher Right Exact Probability |
|
computeKolmogorovAsymptoticProbability()
|
Asymptotic p values for Kolmogorov-Smirnov test for compare two samples |
|
computeKolmogorovExactProbability()
|
Exact p values for Kolmogorov-Smirnov test for compare two samples |
|
computeNumberOfRunsLeftTailProbability()
|
Exact Left tail probability fo number of runs distribution |
|
computeNumberOfRunsRightTailProbability()
|
Exact Left tail probability fo number of runs distribution |
|
computeNumberOfUpDownRunsAsymptoticProbability()
|
Asymptotic values of runs up and down test |
|
computePageAsymptoticProbability()
|
Compute asymptotic probability of distribution |
|
computePageExactProbability()
|
Compute exact probability of Page distribution |
|
computeRunsUpDownExactProbability()
|
Exact tail probability fo number of runs distribution |
|
computeTotalNumberOfRunsAsymptoticProbability()
|
Asymptotic values |
|
computeVonNewmannExactProbability()
|
Exact tail probability for Von Neumann distribution |
|
computeWilcoxonAsymptoticProbability()
|
Compute asymptotic probability of Wilcoxon distribution |
|
computeWilcoxonExactProbability()
|
Compute exact probability of Wilcoxon distribution |
|
computeWilcoxonRankLeftProbability()
|
Wilcoxon exact left p-value |
|
computeWilcoxonRankPValues()
|
Wilcoxon Rank p-values |
|
computeWilcoxonRankRightProbability()
|
Wilcoxon exact right p-value |
|
concordanceCoeff.test()
|
Concordance Coefficient test for multiple comparisons |
|
confidenceQuantile.test()
|
Confidence Quantile for one sample |
|
contingency.coeff.test()
|
Contingency Coefficient test for count data |
|
controlMedian.test()
|
Control Median test for two samples |
|
create.permutations()
|
Create Permutations |
|
danielTrend.test()
|
Daniel Trend test for bivariated samples |
|
dataTable()
|
Java DataTable object |
|
davidBarton.test()
|
David Barton test for scale |
|
.onLoad()
|
Automatic execution function on load of package |
|
doubleTailProbability()
|
Double tail probability |
|
extendedMedian.test()
|
Extended Median test for equality |
|
fisher.test()
|
Fisher test for counts of data |
|
freundAnsariBradley.test()
|
Freund Ansari Bradley test for scale |
|
friedman.test()
|
Friedman test for multiple comparisons |
|
friedmanAR.test()
|
Friedman Aligned-Rank |
|
getCumulativeProbabilityFunction()
|
Get cumulative probability function |
|
getDiff()
|
Auxiliar function to get the difference between observations |
|
getFromDistributionTable()
|
Get value from distribution table |
|
heaviside()
|
Heaviside step function |
|
htest2Tex(<PosteriorDirichlet>)
|
Test object to table in LaTeX format |
|
htest2Tex(<PosteriorIDP>)
|
Test object to table in LaTeX format |
|
htest2Tex(<PosteriorT>)
|
Test object to table in LaTeX format |
|
htest2Tex()
|
Test object to table in LaTeX format |
|
htest2Tex(<htest>)
|
Test object to table in LaTeX format |
|
htest2Tex(<list>)
|
Test object to table in LaTeX format |
|
imanDavenport.test()
|
Iman-Davenport test |
|
incompleteConcordance.test()
|
Incomplete Concordance test for multiple comparisons |
|
jt.test()
|
Jonckheere and Terpstra test for equality |
|
kendall.test()
|
Kendall test for bivariated samples |
|
klotz.test()
|
Klotz test for scale |
|
kruskalWallis.test()
|
Kruskal-Wallis est for equality |
|
ks.test()
|
Kolmogorov-Smirnov test for goodness of fit |
|
ksTwoSamples.test()
|
Kolmogorov-Smirnov test for compare two samples |
|
lilliefors.test()
|
Lilliefors test for goodness of fit |
|
locate.max()
|
Location of the maximum(s) in a vector |
|
make.htest()
|
Make a htest object |
|
mcNemar.test()
|
McNemar test for counts of data |
|
mood.test()
|
Mood test for scale |
|
multinomialEq.test()
|
Multinomial equality test for count data |
|
multipleMeasuresGLRT()
|
Generalized Likelihood Ratio Test for Multiple Performance
Measures |
|
normalScores.test()
|
Normal scores test for location |
|
numberRuns.test()
|
Number of runs test for randomness |
|
numberRunsUpDown.test()
|
Number of runs up and down median test for randomness |
|
numberRunsUpDownMedian.test()
|
Number of runs up and down median test for randomness |
|
numericSequence()
|
Create a numeric sequence |
|
occurencesDominanceConfiguration()
|
Occurences Dominance Configuration |
|
orderedEq.test()
|
Ordered equality test for count data |
|
page.test()
|
Page test for multiple comparisons |
|
partialcorrelation.test()
|
Partial Correlation test for multiple comparisons |
|
pkolmogorov()
|
Kolmogorov probability |
|
plot(<PosteriorDirichlet>)
|
Plot posterior Dirichlet distribution |
|
plot(<PosteriorIDP>)
|
Plot of posterior distributions of IDP |
|
plot(<PosteriorT>)
|
Plot of posterior t distribution |
|
populationQuantile.test()
|
Population quantile for one sample |
|
results
|
Dataset with CV mean results of some algorithms in different datasets |
|
results.knn
|
Dataset with CV results of KNN algorithm in different datasets |
|
results.lr
|
Dataset with CV results of logistic regression algorithm in different datasets |
|
results.nb
|
Dataset with CV results of Naive Bayes algorithm in different datasets |
|
results.nnet
|
Dataset with CV results of neural network algorithm in different datasets |
|
results.rf
|
Dataset with CV results of Random Forest algorithm in different datasets |
|
runTest()
|
Execution of a StatisticalTest object |
|
siegelTukey.test()
|
Siegel-Tukey test for scale |
|
stringSequence()
|
Create a string sequence |
|
sukhatme.test()
|
Sukhatme test for scale |
|
twoSamplesMedian.test()
|
Median test for two samples |
|
vonNeumann.test()
|
Von Neumann test for randomness |
|
waldWolfowitz.test()
|
Wald-Wolfowitz test for compare two samples |
|
waldWolfowitzSequence()
|
Wald-Wolfowitz char vector construction |
|
wilcoxon.test()
|
Wilcoxon Test for one sample |
|
wilcoxonRankSum.test()
|
Wilcoxon Rank Sum test for location |
|
xmlDistributionToDataFrame()
|
Parse XML distribution to a data frame |
|
xmlQuantileDistributionToMatrix()
|
Parse XML quantile distribution to a data frame |