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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 18 days ago.
data-drivenepidemiologyhigh-performance-computingmarkov-chainmathematical-modellinggslopenmp
35 stars 10.09 score 227 scripts
pbiecek
ddst:Data Driven Smooth Tests
Smooth tests are data driven (alternative hypothesis is dynamically selected based on data). In this package you will find two groups of smooth of test: goodness-of-fit tests and nonparametric tests for comparing distributions. Among goodness-of-fit tests there are tests for exponent, Gaussian, Gumbel and uniform distribution. Among nonparametric tests there are tests for stochastic dominance, k-sample test, test with umbrella alternatives and test for change-point problems.
Maintained by Przemyslaw Biecek. Last updated 2 years ago.
data-drivensmooth-teststatisticstest
6 stars 5.26 score 6 scripts 2 dependents
sergejruff
Virusparies:Visualize and Process Output from 'VirusHunterGatherer'
A collection of tools for downstream analysis of 'VirusHunterGatherer' output. Processing of hittables and plotting of results, enabling better interpretation, is made easier with the provided functions.
Maintained by Ruff Sergej. Last updated 4 months ago.
bioinformaticsdata-drivendiscoverdiscoveryggplot2graphical-tablehidden-markov-modelhmmlearnplotr-programmingsummary-statisticsvirusvirus-discoveryvirus-scanningvirusgatherervirushuntervirushuntergatherervisualization
1 stars 4.49 score 28 scripts