Software Library

PCIC has developed a number of R packages for use with climate data, either to solve problems encountered when working with it in R, or to improve upon existing software. PCIC makes these packages freely available under the LGPL or GPL licenses.

climdex.pcic:

The climdex.pcic package lets users of the R programming language calculate CLIMDEX climate extremes indices on climate data of their choosing . It is a relatively fast, well tested implementation of the CLIMDEX indices

. If you want to know more or download the software, please visit http://pacificclimate.github.io/climdex.pcic/.

ClimDown:

ClimDown is a package for the R programming language that allows users to downscale daily climate model output. It contains a suite of routines for downscaling coarse scale global climate model (GCM) output to a fine spatial resolution. It includes implementations of multiple techniques including Constructed Analogues (CA), Climate Imprint (CI), and Bias Correction/Constructed Analogues with Quantile mapping reordering (BCCAQ).

If you want to know more or download the software, please visit http://cran.r-project.org/web/packages/ClimDown/.

udunits2:

The udunits2 package lets users of the R programming language convert data between different units (for example, from degrees C to degrees K).

If you want to know more or download the software, please visit http://cran.r-project.org/web/packages/udunits2/ .

climdex.pcic.ncdf:

The climdex.pcic.ncdf package lets users of the R programming language calculate CLIMDEX climate extremes indices on NetCDF gridded input files in parallel, using an MPI cluster if available. If you want to know more or download the software, please visit http://pacificclimate.github.io/climdex.pcic.ncdf/. Or, download the software directly as a tarball. 

zyp:

The zyp package aids with the determination of trends. The package uses an efficient implementation of Sen's slope method (Sen, 1968) to calculate trend magnitude and provides two options for removing lag-1 autocorrelation (the correlation of a given time series with its own earlier values) and computing the significance of trend: Xuebin Zhang's (Zhang, 1999) and Yue-Pilon's (Yue, 2002). It is available from http://cran.r-project.org/web/packages/zyp/index.html.