Martin Schlather
7 packages on CRAN
The classical Bass (1969) <doi:10.1287/mnsc.15.5.215> model and the agent based models, such as that by Goldenberg, Libai and Muller (2010) <doi:10.1016/j.ijresmar.2009.06.006> have been two different approaches to model adoption processes in marketing. These two approaches can be unified by explicitly modelling the utility functions. This package provides a GUI that allows, in a unified way, the modelling of these two processes and other processes.
This is a collection of fast tools for application in quantitative genetics. For instance, the SNP matrix can be stored in a minimum of memory and the calculation of the genomic relationship matrix is based on a rapid algorithm. It also contains the window scanning approach by Kabluchko and Spodarev (2009), <doi:10.1239/aap/1240319575> to detect anomalous genomic areas <doi:10.1186/s12864-018-5009-y>. Furthermore, the package is used in the Modular Breeding Program Simulator (MoBPS, <https://github.com/tpook92/MoBPS>, <http://www.mobps.de/>). The tools are based on SIMD (Single Instruction Multiple Data, <https://en.wikipedia.org/wiki/SIMD>) and OMP (Open Multi-Processing, <https://de.wikipedia.org/wiki/OpenMP>).
Methods for the inference on and the simulation of Gaussian fields are provided, as well as methods for the simulation of extreme value random fields.
Various utilities are provided that might be used in spatial statistics and elsewhere. It delivers a method for solving linear equations that checks the sparsity of the matrix before any algorithm is used. Furthermore, it includes the Struve functions.
This gui shows realisations of times series, currently ARMA and GARCH processes. It might be helpful for teaching and studying.
Given an univariate dataset, returns the best fitting parameter families, as defined in Shao (2003) <doi:10.1007/B97553>, including their parameter estimates via maximum likelihood.
Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) <doi:10.1007/978-0-387-48536-2>.