Package: MKclass 0.5

MKclass: Statistical Classification

Performance measures and scores for statistical classification such as accuracy, sensitivity, specificity, recall, similarity coefficients, AUC, GINI index, Brier score and many more. Calculation of optimal cut-offs and decision stumps (Iba and Langley (1991), <doi:10.1016/B978-1-55860-247-2.50035-8>) for all implemented performance measures. Hosmer-Lemeshow goodness of fit tests (Lemeshow and Hosmer (1982), <doi:10.1093/oxfordjournals.aje.a113284>; Hosmer et al (1997), <doi:10.1002/(SICI)1097-0258(19970515)16:9%3C965::AID-SIM509%3E3.0.CO;2-O>). Statistical and epidemiological risk measures such as relative risk, odds ratio, number needed to treat (Porta (2014), <doi:10.1093%2Facref%2F9780199976720.001.0001>).

Authors:Matthias Kohl [aut, cre]

MKclass_0.5.tar.gz
MKclass_0.5.zip(r-4.5)MKclass_0.5.zip(r-4.4)MKclass_0.5.zip(r-4.3)
MKclass_0.5.tgz(r-4.4-any)MKclass_0.5.tgz(r-4.3-any)
MKclass_0.5.tar.gz(r-4.5-noble)MKclass_0.5.tar.gz(r-4.4-noble)
MKclass_0.5.tgz(r-4.4-emscripten)MKclass_0.5.tgz(r-4.3-emscripten)
MKclass.pdf |MKclass.html✨
MKclass/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/stamats/mkclass/issues

On CRAN:

4.23 score 2 stars 17 scripts 269 downloads 100 exports 0 dependencies

Last updated 1 years agofrom:b595b79e1f. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-winOKNov 10 2024
R-4.5-linuxOKNov 10 2024
R-4.4-winNOTENov 10 2024
R-4.4-macNOTENov 10 2024
R-4.3-winNOTENov 10 2024
R-4.3-macNOTENov 10 2024

Exports:ACCAUCAUC.testBACCBBSBFG0BFG1BLRBPVBSBSSCKCconfMatrixCRVCSIdecisionStumpDFMDORDPDPpDPREVDRDSCEACCECERF1SFARFBSFCFDRFICFNRFOFORFPRGINIHLgof.testHRINFINFQRJDJE2JSCMARKMCCMI2MRNBSNIRNLRNPOSTONPOSTPNPREONPREPNPVNUoptCutoffor2rrpairwise.aucPBSPCCPDperfMeasuresperfScoresPFAPHICPLRPMCPOSTOPOSTPPPPPPVPRECpredValuesPREOPREPPREVPROFRECREDrisksRPHIrrCIRSISELSENSSMCSPECSUTNRTPRTSUCVI2WACCWBSWLRWPVYJS

Dependencies:

Package MKclass

Matthias Kohl

Rendered fromMKclass.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2022-12-01
Started: 2019-08-16

Readme and manuals

Help Manual

Help pageTopics
Statistical Classification.MKclass-package MKclass
Compute AUCAUC
AUC-TestAUC.test
Compute Confusion MatrixconfMatrix
Compute Decision StumpsdecisionStump
Hosmer-Lemeshow goodness of fit tests.HLgof.test
Compute the Optimal Cutoff for Binary ClassificationoptCutoff
Transform OR to RRor2rr
Compute pairwise AUCspairwise.auc
Compute Performance Measures or Binary ClassificationACC BACC BFG0 BFG1 BLR BPV CKC CRV CSI DFM DOR DP DPp DPREV DR DSC EACC EC ER F1S FAR FBS FC FDR FIC FNR FO FOR FPR HR INF INFQR JD JE2 JSC MARK MCC MI2 MR NIR NLR NPOSTO NPOSTP NPREO NPREP NPV NU PCC PD perfMeasures PFA PHIC PLR PMC POSTO POSTP PPP PPV PREC PREO PREP PREV PROF REC RED RPHI RSI SEL SENS SMC SPEC SU TNR TPR TS UC VI2 WACC WLR WPV YJS
Compute Performance Scores for Binary ClassificationBBS BS BSS GINI NBS PBS perfScores WBS
Compute PPV and NPV.predValues
Compute RR, OR and Other Risk Measuresrisks
Compute Approximate Confidence Interval for RR.rrCI