Showing 6 of total 6 results (show query)
brodieg
unitizer:Interactive R Unit Tests
Simplifies regression tests by comparing objects produced by test code with earlier versions of those same objects. If objects are unchanged the tests pass, otherwise execution stops with error details. If in interactive mode, tests can be reviewed through the provided interactive environment.
Maintained by Brodie Gaslam. Last updated 13 days ago.
39 stars 7.48 score 84 scripts
nproellochs
ReinforcementLearning:Model-Free Reinforcement Learning
Performs model-free reinforcement learning in R. This implementation enables the learning of an optimal policy based on sample sequences consisting of states, actions and rewards. In addition, it supplies multiple predefined reinforcement learning algorithms, such as experience replay. Methodological details can be found in Sutton and Barto (1998) <ISBN:0262039249>.
Maintained by Nicolas Proellochs. Last updated 5 years ago.
experience-samplingreinforcement-learning
69 stars 7.34 score 210 scripts 1 dependents
trinker
wakefield:Generate Random Data Sets
Generates random data sets including: data.frames, lists, and vectors.
Maintained by Tyler Rinker. Last updated 5 years ago.
256 stars 7.13 score 209 scripts
miguel-porto
SiMRiv:Simulating Multistate Movements in River/Heterogeneous Landscapes
Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers, heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as mechanistic null models (Potts & Lewis 2014, <DOI:10.1098/rspb.2014.0231>) to test a variety of 'Movement Ecology' hypotheses (Nathan et al. 2008, <DOI:10.1073/pnas.0800375105>), including landscape effects (e.g. resources, infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk, dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species spread, and population dynamics.
Maintained by Miguel Porto. Last updated 7 months ago.
animal-movementheterogeneous-landscapesmovement-ecologyriver-networkssimulation
15 stars 6.08 score 27 scripts 1 dependents
bioc
oligoClasses:Classes for high-throughput arrays supported by oligo and crlmm
This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages.
Maintained by Benilton Carvalho. Last updated 5 months ago.
5.86 score 93 scripts 17 dependentsbioc
VanillaICE:A Hidden Markov Model for high throughput genotyping arrays
Hidden Markov Models for characterizing chromosomal alteration in high throughput SNP arrays.
Maintained by Robert Scharpf. Last updated 5 months ago.
5.36 score 63 scripts 1 dependents