0. Quasi-likelihood parameter estimation- The package provides methods for parameter estimation for statistical models that can be simulated. We follow a quasi-likelihood approach to estimate the unknown parameter by the root of the so-called quasi-score estimating function.
2. Test for point containment in polyhedron- This project provides a function to test whether a point is contained within a closed three-dimensional polyhedron, defined by a triangular mesh. A two-dimensional version for polygons is also provided.
3. move: analyze animal movement data- move is a package that contains functions to access movement data stored in movebank.org as well as tools to visualize and statistically analyse animal movement data. Move addresses movement ecological questions.
4. grainscape- Create grains of connectivity and minimum planar graph models to calculate effective distances for landscape connectivity at multiple scales.
**Development of this package has moved to GitHub (https://achubaty.github.io/grainscape).**
Topic : Spatial Data & Statistics : Ecological Analysis [Filter]
Registered: 2012-03-07 12:18
5. Distance Weighted Discrimination (DWD)- Distance Weighted Discrimination (DWD) is a machine learning tool similar to SVM but with better breakdown properties as the number of variables increases in relationship to the number of observations.
7. lossDev- A Bayesian time series loss development model. Features include skewed-t distribution with time-varying scale parameter, Reversible Jump MCMC for determining the functional form of the consumption path, and a structural break in this path.
10. libamtrack- This package is the R interface to the open-source, ANSI C library libamtrack (http://libamtrack.dkfz.org). It provides computational routines for research in proton and ion beam therapy.
12. rPithon: use Python over a 'pipe'- rPithon is based on rPython but instead of linking to the Python libraries, a Python program is started and is communicated with using a 'pipe'. This allows different R programs/packages to use different Python instances if desired.