Predictor Identifier: Nonparametric PREDiction (NPRED) Partial informational correlation (PIC) is used to identify the meaningful predictors to the response from a large set of potential predictors.

The initial version of NPRED is at Hydrology@UNSW. This is a new version of NPRED without calling Fortran codes.

Applications of this package can be found in: * Jiang, Z., Sharma, A., & Johnson, F. (2021). Variable transformations in the spectral domain – Implications for hydrologic forecasting. Journal of Hydrology, 126816. doi * Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020). A wavelet-based tool to modulate variance in predictors: an application to predicting drought anomalies. Environmental Modelling & Software, 135, 104907. doi * Jiang, Z., Sharma, A., & Johnson, F. (2020). Refining Predictor Spectral Representation Using Wavelet Theory for Improved Natural System Modeling. Water Resources Research, 56(3), e2019WR026962. doi


You can install the package via devtools from GitHub with:


or via CRAN with:



Sharma, A., Mehrotra, R. (2014). An information theoretic alternative to model a natural system using observational information alone. Water Resources Research, 50(1): 650-660.

Galelli S., Humphrey G.B., Maier H.R., Castelletti A., Dandy G.C. and Gibbs M.S. (2014). An evaluation framework for input variable selection algorithms for environmental data-driven models, Environmental Modelling and Software, 62, 33-51.

Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). A programming tool for nonparametric system prediction using Partial Informational Correlation and Partial Weights. Environmental Modelling & Software, 83, 271-275.

Mehrotra, R., & Sharma, A. (2006). Conditional resampling of hydrologic time series using multiple predictor variables: A K-nearest neighbour approach. Advances in Water Resources, 29(7), 987-999.