Title: Manage, Analyse and Simulate Hyperspectral Data in R
Maintainer: Lukas W. Lehnert <lukaslehnert@googlemail.com>
Depends: R (>= 2.10.0), sp, raster, rgdal, rootSolve, signal, methods
Description: Transformation of reflectance spectra, calculation of vegetation indices and red edge parameters, spectral resampling for hyperspectral remote sensing, simulation of reflectance and transmittance using PROSPECT and PROSAIL.

Description: Transformation of reflectance spectra, calculation of vegetation indices and red edge parameters, spectral resampling for hyperspectral remote sensing, simulation of reflectance and transmittance using the leaf reflectance model PROSPECT and the canopy reflectance model PROSAIL.

Maintainer: Lukas W. Lehnert <fightgravity@posteo.de>

Title: Manage, Analyse and Simulate Hyperspectral Data

Depends: R (>= 2.10.0), raster, rgdal, rootSolve, signal, methods,

Suggests: rgl, RCurl, pracma, foreach, hyperSpec

Suggests: rgl, RCurl, pracma (>= 2.0.1), foreach, hyperSpec

Suggests: rgl (>= 0.98.1), RCurl, pracma (>= 2.0.1), foreach, hyperSpec

Suggests: rgl (>= 0.98.1), RCurl, foreach, hyperSpec

person("Lukas W.", "Lehnert", role = c("cre", "aut"),
Author: Lukas W. Lehnert [cre, aut] (<https://orcid.org/0000-0002-5229-2282>),

Suggests: rgl (>= 0.98.1), RCurl, foreach, asdreader

Description: Density (probability), distribution, inverse
distribution (quantile) and random data generation
functions for the hse ("hope springs eternal") distribution.
Functions to calculate rough moment estimates, and maximum
likelihood estimates of the parameters of this distribution.
Functions to plot the probabilities of the distribution,
given the parameter values, and to plot the fitted
probabilities given a maximum likelihood fit.

Maximum likelihood estimation of parameters.  Plotting of
the probability mass function, from specified parameters or
from a maximum likelihood fit, and of the likelihood surface.
Fisher information, different estimates of the hessian of the
log likelihood and Monte Carlo estimation of the covariance