Package: qualV 0.3-5
qualV: Qualitative Validation Methods
Qualitative methods for the validation of dynamic models. It contains (i) an orthogonal set of deviance measures for absolute, relative and ordinal scale and (ii) approaches accounting for time shifts. The first approach transforms time to take time delays and speed differences into account. The second divides the time series into interval units according to their main features and finds the longest common subsequence (LCS) using a dynamic programming algorithm.
Authors:
qualV_0.3-5.tar.gz
qualV_0.3-5.zip(r-4.5)qualV_0.3-5.zip(r-4.4)qualV_0.3-5.zip(r-4.3)
qualV_0.3-5.tgz(r-4.4-x86_64)qualV_0.3-5.tgz(r-4.4-arm64)qualV_0.3-5.tgz(r-4.3-x86_64)qualV_0.3-5.tgz(r-4.3-arm64)
qualV_0.3-5.tar.gz(r-4.5-noble)qualV_0.3-5.tar.gz(r-4.4-noble)
qualV_0.3-5.tgz(r-4.4-emscripten)qualV_0.3-5.tgz(r-4.3-emscripten)
qualV.pdf |qualV.html✨
qualV/json (API)
NEWS
# Install 'qualV' in R: |
install.packages('qualV', repos = c('https://r-forge.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://r-forge.r-project.org/projects/qualv
Last updated 1 years agofrom:18b6e20603. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Sep 06 2024 |
R-4.5-win-x86_64 | OK | Sep 06 2024 |
R-4.5-linux-x86_64 | OK | Sep 06 2024 |
R-4.4-win-x86_64 | OK | Sep 06 2024 |
R-4.4-mac-x86_64 | OK | Sep 06 2024 |
R-4.4-mac-aarch64 | OK | Sep 06 2024 |
R-4.3-win-x86_64 | OK | Sep 06 2024 |
R-4.3-mac-x86_64 | OK | Sep 06 2024 |
R-4.3-mac-aarch64 | OK | Sep 06 2024 |
Exports:CMAECMSEcompareMEEFf.curvef.levelf.slopef.steepgeneralMEGRILCSMAEMAGEMALEMAOEMAPEMSEMSLEMSOEplot.qvalLCSplot.timeTransMEprint.compareMEprint.qvalLCSprint.timeTransMEqvalLCSRCMSERMSERMSGERMSLERMSOERSMSERSMSGERSMSLESMAESMAGESMALESMSESMSLEsummary.compareMEsummary.qvalLCSsummary.timeTransMEtimeTransMEtransBetatransBeziertransSimplex
Dependencies:KernSmooth
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Qualitative Validation Methods | qualV-package qualV |
Compute Several Deviance Measures for Comparison | compareME print.compareME summary.compareME |
Efficiency Factor as Suggested by Nash and Sutcliffe | EF |
Qualitative Features of Time Series | f.curve f.level f.slope f.steep features |
Geometric Reliability Index as Suggested by Leggett and Williams (1981) | GRI |
Algorithm for the Longest Common Subsequence Problem | LCS |
Observed and Predicted Data of Phytoplankton | obs phyto sim |
Quantitative Validation Methods | CMAE CMSE generalME MAE MAGE MALE MAOE MAPE MSE MSLE MSOE RCMSE RMSE RMSGE RMSLE RMSOE RSMSE RSMSGE RSMSLE SMAE SMAGE SMALE SMSE SMSLE |
Qualitative Validation by Means of Interval Sequences and LCS | plot.qvalLCS print.qvalLCS qvalLCS summary.qvalLCS |
Bijective Transformations of Time | transBeta transBezier transSimplex |
Transformation of Time to Match Two Time Series | plot.timeTransME print.timeTransME summary.timeTransME timeTransME timetransme |