TThe package provides several exhaustive high-performance functions to perform quantile matching (QM; see for example Panofsky and Brier, 1968 and Themeßl et al., 2011) of an actual distribution and a target distribution. It also provides the functions for the non-stationary case (see for example Michelangeli et al., 2009).
See the Package Documentation for details and examples.
The package can be installed with the Julia package manager as follows:
julia> import Pkg
julia> Pkg.add("QuantileMatching")Contributions are welcomed. Here's the workflow for development of new features, refactoring and bugfix.
main # Stable branch, always ready to be tagged
dev # Development branch. New features, refactoring, bug and hotfix
# are integrated into dev before going into master.
feature/<feature-name> # New feature needs a `feature` prefix
refactor/<refactor-name> # Refactoring are tagged with a `refactor` prefix
bug/<bug-fix> # Prefix for bugs found during development
hotfix/<hot-fix> # Prefix for hotfix (bugs for deployed versions)
Michelangeli, P.-A., Vrac, M., and Loukos, H. (2009), Probabilistic downscaling approaches: Application to wind cumulative distribution functions, Geophys. Res. Lett., 36, L11708, doi:10.1029/2009GL038401.
Panofsky, H. and Brier, G. (1968). Some Applications of Statistics to Meteorology, Earth and Mineral Sciences Continuing Education, College of Earth and Mineral Sciences, The Pennsylvania State University.
Themeßl, M., Gobiet, A. and Leuprecht, A. (2011). Empirical-statistical downscaling and error correction of daily precipitation from regional climate models, International Journal of Climatology, 31, 1530–1544, https://doi.org/https://doi.org/10.1002/joc.2168.