Package: NPRED 1.1.0

Ze Jiang

NPRED: Predictor Identifier: Nonparametric Prediction

Partial informational correlation (PIC) is used to identify the meaningful predictors to the response from a large set of potential predictors. Details of methodologies used in the package can be found in Sharma, A., Mehrotra, R. (2014). <doi:10.1002/2013WR013845>, Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). <doi:10.1016/j.envsoft.2016.05.021>, and Mehrotra, R., & Sharma, A. (2006). <doi:10.1016/j.advwatres.2005.08.007>.

Authors:Ashish Sharma [aut], Raj Mehrotra [aut], Sanjeev Jha [aut], Jingwan Li [aut], Ze Jiang [aut, cre]

NPRED_1.1.0.tar.gz
NPRED_1.1.0.tar.gz(r-4.5-noble)NPRED_1.1.0.tar.gz(r-4.4-noble)
NPRED_1.1.0.tgz(r-4.4-emscripten)NPRED_1.1.0.tgz(r-4.3-emscripten)
NPRED.pdf |NPRED.html✨
NPRED/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/zejiang-unsw/npred/issues

Datasets:
  • Sydney - Sample data: Data over Sydney region
  • data1 - Sample data : AR9 model: x(i)=0.3*x(i-1)-0.6*x(i-4)-0.5*x(i-9)+eps
  • data2 - Sample data : AR4 model: x(i)=0.6*x(i-1)-0.4*x(i-4)+eps
  • data3 - Sample data : AR1 model: x(i)=0.9*x(i-1)+0.866*eps

9 exports 0.00 score 0 dependencies 19 scripts 1.3k downloads

Last updated 1 months agofrom:eb0824c94d. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKAug 25 2024
R-4.5-linuxOKAug 25 2024

Exports:calc.scaleSTDratiodata.gen.ar1data.gen.ar4data.gen.ar9knnknnregl1cvpic.calcpw.calcstepwise.PIC

Dependencies:

Predictor Identifier: Nonparametric PREDiction

Ashish Sharma, Raj Mehrotra, Sanjeev Jha, Jingwan Li and Ze Jiang

Rendered fromNPRED.Rmdusingknitr::rmarkdownon Aug 25 2024.

Last update: 2024-07-26
Started: 2021-08-13