Showing 8 of total 8 results (show query)
spedygiorgio
markovchain:Easy Handling Discrete Time Markov Chains
Functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>. Some functions for continuous times Markov chains depend on the suggested ctmcd package.
Maintained by Giorgio Alfredo Spedicato. Last updated 4 months ago.
ctmcdtmcmarkov-chainmarkov-modelr-programmingrcppopenblascpp
22.4 match 104 stars 12.78 score 712 scripts 4 dependents
ehanks
ctmcmove:Modeling Animal Movement with Continuous-Time Discrete-Space Markov Chains
Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.
Maintained by Ephraim Hanks. Last updated 2 months ago.
11.8 match 1 stars 1.78 score 30 scripts
dsjohnson
walk:Model Animal Movement With Continuous-Time Markov Chains
Model animal movement using continuous-time Markov chain models.
Maintained by Devin S. Johnson. Last updated 11 months ago.
5.2 match 2.30 score 1 scripts
cran
modesto:Modeling and Analysis of Stochastic Systems
Compute important quantities when we consider stochastic systems that are observed continuously. Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press.
Maintained by Carlos Alberto Cardozo Delgado. Last updated 3 years ago.
9.9 match 1.00 score
marshalllab
MGDrivE2:Mosquito Gene Drive Explorer 2
A simulation modeling framework which significantly extends capabilities from the 'MGDrivE' simulation package via a new mathematical and computational framework based on stochastic Petri nets. For more information about 'MGDrivE', see our publication: <https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13318>. Some of the notable capabilities of 'MGDrivE2' include: incorporation of human populations, epidemiological dynamics, time-varying parameters, and a continuous-time simulation framework with various sampling algorithms for both deterministic and stochastic interpretations. 'MGDrivE2' relies on the genetic inheritance structures provided in package 'MGDrivE', so we suggest installing that package initially.
Maintained by Sean L. Wu. Last updated 4 years ago.
1.3 match 6 stars 6.33 score 30 scripts
okamumu
mapfit:PH/MAP Parameter Estimation
Estimation methods for phase-type distribution (PH) and Markovian arrival process (MAP) from empirical data (point and grouped data) and density function. The tool is based on the following researches: Okamura et al. (2009) <doi:10.1109/TNET.2008.2008750>, Okamura and Dohi (2009) <doi:10.1109/QEST.2009.28>, Okamura et al. (2011) <doi:10.1016/j.peva.2011.04.001>, Okamura et al. (2013) <doi:10.1002/asmb.1919>, Horvath and Okamura (2013) <doi:10.1007/978-3-642-40725-3_10>, Okamura and Dohi (2016) <doi:10.15807/jorsj.59.72>.
Maintained by Hiroyuki Okamura. Last updated 2 years ago.
2.3 match 2 stars 3.34 score 22 scriptsdsjohnson
moveMMPP:Fit Continuous-Time Markov Modulated Poisson Process Movement Models to Animal Resight Data
Using animal resight data, the package functions allow the user to estimate movement rates and resource selection using a continuous-time Markov chain movement model.
Maintained by Devin Johnson. Last updated 11 months ago.
1.7 match 1.48 score
