Introduction | OR, RR and Other Risk Measures | AUC | Estimation | Testing | Pairwise | PPV and NPV | Performance Measures and Scores | Optimal Cutoff | Hosmer-Lemeshow and le Cessie-van Houwelingen-Copas-Hosmer | sessionInfo
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:
MKclass_0.5.tar.gz
MKclass_0.5.zip(r-4.7)MKclass_0.5.zip(r-4.6)MKclass_0.5.zip(r-4.5)
MKclass_0.5.tgz(r-4.6-any)MKclass_0.5.tgz(r-4.5-any)
MKclass_0.5.tar.gz(r-4.7-any)MKclass_0.5.tar.gz(r-4.6-any)
MKclass_0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MKclass/json (API)
| # Install 'MKclass' in R: |
| install.packages('MKclass', repos = c('https://stamats.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/stamats/mkclass/issues
Last updated from:c01b704dca. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 110 | ||
| source / vignettes | OK | 151 | ||
| linux-release-x86_64 | OK | 110 | ||
| macos-release-arm64 | OK | 86 | ||
| macos-oldrel-arm64 | OK | 76 | ||
| windows-devel | OK | 81 | ||
| windows-release | OK | 79 | ||
| windows-oldrel | OK | 76 | ||
| wasm-release | OK | 93 |
Exports:ACCAUCAUC.testBACCBBSBFG0BFG1BLRBPVBSBSSCKCconfMatrixCRVCSIdecisionStumpDFMDORDPDPpDPREVDRDSCEACCECERF1SFARFBSFCFDRFICFNRFOFORFPRGINIHLgof.testHRINFINFQRJDJE2JSCMARKMCCMI2MRNBSNIRNLRNPOSTONPOSTPNPREONPREPNPVNUoptCutoffor2rrpairwise.aucPBSPCCPDperfMeasuresperfScoresPFAPHICPLRPMCPOSTOPOSTPPPPPPVPRECpredValuesPREOPREPPREVPROFRECREDrisksRPHIrrCIRSISELSENSSMCSPECSUTNRTPRTSUCVI2WACCWBSWLRWPVYJS
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Statistical Classification. | MKclass-package MKclass |
| Compute AUC | AUC |
| AUC-Test | AUC.test |
| Compute Confusion Matrix | confMatrix |
| Compute Decision Stumps | decisionStump |
| Hosmer-Lemeshow goodness of fit tests. | HLgof.test |
| Compute the Optimal Cutoff for Binary Classification | optCutoff |
| Transform OR to RR | or2rr |
| Compute pairwise AUCs | pairwise.auc |
| Compute Performance Measures or Binary Classification | ACC 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 Classification | BBS BS BSS GINI NBS PBS perfScores WBS |
| Compute PPV and NPV. | predValues |
| Compute RR, OR and Other Risk Measures | risks |
| Compute Approximate Confidence Interval for RR. | rrCI |
