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 5 months ago.
ctmcdtmcmarkov-chainmarkov-modelr-programmingrcppopenblascpp
104 stars 12.78 score 712 scripts 4 dependents
stewid
SimInf:A Framework for Data-Driven Stochastic Disease Spread Simulations
Provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and 'OpenMP' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) <doi:10.18637/jss.v091.i12>. The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) <doi:10.1098/rsif.2008.0172>.
Maintained by Stefan Widgren. Last updated 16 days ago.
data-drivenepidemiologyhigh-performance-computingmarkov-chainmathematical-modellinggslopenmp
35 stars 10.09 score 227 scripts
eheinzen
elo:Ranking Teams by Elo Rating and Comparable Methods
A flexible framework for calculating Elo ratings and resulting rankings of any two-team-per-matchup system (chess, sports leagues, 'Go', etc.). This implementation is capable of evaluating a variety of matchups, Elo rating updates, and win probabilities, all based on the basic Elo rating system. It also includes methods to benchmark performance, including logistic regression and Markov chain models.
Maintained by Ethan Heinzen. Last updated 1 years ago.
eloelo-ratinglogistic-regressionmarkov-chainmarkov-modelrankingsports-analyticscpp
37 stars 7.05 score 153 scripts
archaeostat
ArchaeoPhases:Post-Processing of Markov Chain Monte Carlo Simulations for Chronological Modelling
Statistical analysis of archaeological dates and groups of dates. This package allows to post-process Markov Chain Monte Carlo (MCMC) simulations from 'ChronoModel' <https://chronomodel.com/>, 'Oxcal' <https://c14.arch.ox.ac.uk/oxcal.html> or 'BCal' <https://bcal.shef.ac.uk/>. It provides functions for the study of rhythms of the long term from the posterior distribution of a series of dates (tempo and activity plot). It also allows the estimation and visualization of time ranges from the posterior distribution of groups of dates (e.g. duration, transition and hiatus between successive phases) as described in Philippe and Vibet (2020) <doi:10.18637/jss.v093.c01>.
Maintained by Anne Philippe. Last updated 12 months ago.
archaeologybayesian-statisticsgeochronologymarkov-chainradiocarbon-dates
10 stars 6.90 score 66 scripts
fabrice-rossi
mixvlmc:Variable Length Markov Chains with Covariates
Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bühlmann, P. and Wyner, A. J. (1999) <doi:10.1214/aos/1018031204> for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) <doi:10.1111/jtsa.12615> for VLMC with covariates.
Maintained by Fabrice Rossi. Last updated 11 months ago.
machine-learningmarkov-chainmarkov-modelstatisticstime-seriescpp
2 stars 6.23 score 20 scripts
andrewmarx
samc:Spatial Absorbing Markov Chains
Implements functions for working with absorbing Markov chains. The implementation is based on the framework described in "Toward a unified framework for connectivity that disentangles movement and mortality in space and time" by Fletcher et al. (2019) <doi:10.1111/ele.13333>, which applies them to spatial ecology. This framework incorporates both resistance and absorption with spatial absorbing Markov chains (SAMC) to provide several short-term and long-term predictions for metrics related to connectivity in landscapes. Despite the ecological context of the framework, this package can be used in any application of absorbing Markov chains.
Maintained by Andrew Marx. Last updated 5 months ago.
absorbing-markov-chainsconnectivitylandscape-ecologylandscape-metricsmarkov-chaincpp
12 stars 5.26 score 15 scripts
roga11
MSTest:Hypothesis Testing for Markov Switching Models
Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2024) <https://grodriguezrondon.com/files/RodriguezRondon_Dufour_2024_MonteCarlo_LikelihoodRatioTest_MarkovSwitchingModels_20241015.pdf> that can be used to identify the number of regimes in Markov switching models.
Maintained by Gabriel Rodriguez Rondon. Last updated 1 months ago.
autoregressivebootstraphypothesis-testinglikelihood-ratio-testmarkov-chainmomentsmonte-carlonon-linearregime-switchingtime-seriesopenblascppopenmp
5 stars 4.18 score 3 scriptsarchaeostat
ArchaeoChron:Bayesian Modeling of Archaeological Chronologies
Provides a list of functions for the Bayesian modeling of archaeological chronologies. The Bayesian models are implemented in 'JAGS' (Plummer 2003). The inputs are measurements with their associated standard deviations and the study period. The output is the MCMC sample of the posterior distribution of the event date with or without radiocarbon calibration.
Maintained by Anne Philippe. Last updated 1 years ago.
archaeologybayesian-statisticsgeochronologymarkov-chainradiocarbon-datesjagscpp
3 stars 3.65 score 15 scripts