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Building Regression and Classification Models
Consistent user interface to the most common regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, complemented with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.
A Portal for Global Governance Data
This is the core package for the many packages universe. It includes functions to help researchers work with and contribute to event datasets on global governance.
Interpolation Grids for Packages of the 'RobASt' - Family of Packages
Includes 'sysdata.rda' file for packages of the 'RobASt' - family of packages; is currently used by package 'RobExtremes' only.
Optimally Robust Estimation for Extreme Value Distributions
Optimally robust estimation for extreme value distributions using S4 classes and methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and 'ROptEst'); the underlying theoretic results can be found in Ruckdeschel and Horbenko, (2013 and 2012), \doi{10.1080/02331888.2011.628022} and \doi{10.1007/s00184-011-0366-4}.
The P-Model and BiomeE Modelling Framework
Implements the Simulating Optimal FUNctioning framework for
site-scale simulations of ecosystem processes, including model
calibration. It contains 'Fortran 90' modules for the P-model (Stocker
et al. (2020)
Wood Valuation Germany
Monetary valuation of wood in German forests (stumpage values), including estimations of harvest quantities, wood revenues, and harvest costs. The functions are sensitive to tree species, mean diameter of the harvested trees, stand quality, and logging method. The functions include estimations for the consequences of disturbances on revenues and costs. The underlying assortment tables are taken from Offer and Staupendahl (2018) with corresponding functions for salable and skidded volume derived in Fuchs et al. (2023). Wood revenue and harvest cost functions were taken from v. Bodelschwingh (2018). The consequences of disturbances refer to Dieter (2001), Moellmann and Moehring (2017), and Fuchs et al. (2022a, 2022b). For the full references see documentation of the functions, package README, and Fuchs et al. (2023). Apart from Dieter (2001) and Moellmann and Moehring (2017), all functions and factors are based on data from HessenForst, the forest administration of the Federal State of Hesse in Germany.