  Hyperspectral remote sensing is a promising tool for a variety of
applications including ecology, geology, analytical chemistry and medical
research. This article presents the new \hsdar package for R statistical
software, which performs a variety of analysis steps taken during a typical
hyperspectral remote sensing approach. The package introduces a new class for
efficiently storing large hyperspectral datasets such as hyperspectral cubes
within R. The package includes several important hyperspectral analysis tools
such as continuum removal, normalized ratio indices and integrates two widely
used radiation transfer models. In addition, the package provides methods to
directly use the functionality of the caret package for machine learning tasks.
Two case studies demonstrate the package's range of functionality: First, plant
leaf chlorophyll content is estimated and second, cancer in the human larynx is
detected from hyperspectral data.
