Package: QWDAP 1.1.20
Binghuang Pan
QWDAP: Quantum Walk-Based Data Analysis and Prediction
The modeling and prediction of graph-associated time series(GATS) based on continuous time quantum walk. This software is mainly used for feature extraction, modeling, prediction and result evaluation of GATS, including continuous time quantum walk simulation, feature selection, regression analysis, time series prediction, and series fit calculation. A paper is attached to the package for reference.
Authors:
QWDAP_1.1.20.tar.gz
QWDAP_1.1.20.tar.gz(r-4.5-noble)QWDAP_1.1.20.tar.gz(r-4.4-noble)
QWDAP_1.1.20.tgz(r-4.4-emscripten)QWDAP_1.1.20.tgz(r-4.3-emscripten)
QWDAP.pdf |QWDAP.html✨
QWDAP/json (API)
| # Install 'QWDAP' in R: |
| install.packages('QWDAP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- traffic.model.n1 - The estabulished model by Stepwise Regression of the 'N1' station
- traffic.n1 - Data of the 'N1' station
- traffic.qw - A set of modes generated by quantum walk
- trafficflow - Highway traffic flow data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 months agofrom:38c36a1545. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 10 2024 |
| R-4.5-linux-x86_64 | OK | Nov 10 2024 |
Exports:qwdap.evalqwdap.pcrqwdap.plsrqwdap.pprqwdap.predictqwdap.qwalkqwdap.rrelieffqwdap.swrqwdap.swsqwdap.var
Dependencies:clusterCORElearnnnetplotrixplsRcppRcppEigenrpartrpart.plot
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Evaluation | qwdap.eval |
| Principle Component Regression | qwdap.pcr |
| Partial Least Squares Regression | qwdap.plsr |
| Projection Pursuit Regression | qwdap.ppr |
| Prediction | qwdap.predict |
| Quantum Walk | qwdap.qwalk |
| RReliefF | qwdap.rrelieff |
| Model by Stepwise Regression | qwdap.swr |
| Mode Selection by Stepwise Regression | qwdap.sws |
| Vector Autoregressive Model | qwdap.var |
| The estabulished model by Stepwise Regression of the 'N1' station | traffic.model.n1 |
| Data of the 'N1' station | traffic.n1 |
| A set of modes generated by quantum walk | traffic.qw |
| Highway traffic flow data | trafficflow |