Showing 6 of total 6 results (show query)
laplacesdemonr
LaplacesDemon:Complete Environment for Bayesian Inference
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).
Maintained by Henrik Singmann. Last updated 1 years ago.
93 stars 13.45 score 1.8k scripts 60 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 4 hours ago.
data-drivenepidemiologyhigh-performance-computingmarkov-chainmathematical-modellinggslopenmp
35 stars 10.11 score 227 scripts
mjg211
phaseR:Phase Plane Analysis of One- And Two-Dimensional Autonomous ODE Systems
Performs a qualitative analysis of one- and two-dimensional autonomous ordinary differential equation systems, using phase plane methods. Programs are available to identify and classify equilibrium points, plot the direction field, and plot trajectories for multiple initial conditions. In the one-dimensional case, a program is also available to plot the phase portrait. Whilst in the two-dimensional case, programs are additionally available to plot nullclines and stable/unstable manifolds of saddle points. Many example systems are provided for the user. For further details can be found in Grayling (2014) <doi:10.32614/RJ-2014-023>.
Maintained by Michael J Grayling. Last updated 3 years ago.
biological-modelingdifferential-equationsdynamical-systemsecological-modellinglotka-volterramanifoldsmodeling-dynamic-systemsmorris-lecarperturbation-analysisphase-planesir-modelspecies-interactionsvan-der-pol
15 stars 6.63 score 94 scripts 1 dependents
cran
JADE:Blind Source Separation Methods Based on Joint Diagonalization and Some BSS Performance Criteria
Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) <doi:10.18637/jss.v076.i02>.
Maintained by Klaus Nordhausen. Last updated 2 years ago.
2 stars 5.11 score 16 dependents
clement-w
SIRthresholded:Sliced Inverse Regression with Thresholding
Implements a thresholded version of the Sliced Inverse Regression method, which allows to do variable selection.
Maintained by Clement Weinreich. Last updated 6 months ago.
dimensionality-reductioninverse-regressionstatistical-learningvariable-selection
4 stars 4.30 score 4 scriptscran
EnviroPRA2:Environmental Probabilistic Risk Assessment Tools
It contains functions for dose calculation for different routes, fitting data to probability distributions, random number generation (Monte Carlo simulation) and calculation of systemic and carcinogenic risks. For more information see the publication: Barrio-Parra et al. (2019) "Human-health probabilistic risk assessment: the role of exposure factors in an urban garden scenario" <doi:10.1016/j.landurbplan.2019.02.005>.
Maintained by Fernando Barrio-Parra. Last updated 1 years ago.
1.00 score