Open Source S/R Simulation Software

gVirtualXRay is a C++ library to simulate X-ray imaging. It is based on the Beer-Lambert law to compute the absorption of light (i.e. photons) by 3D objects (here polygon meshes). It is implemented on the graphics processing unit (GPU) using the OpenGL Shading Language (GLSL). SimpleGVXR is a smaller library build on the top of gVirtualXRay. It provides wrappers to Python2, Python3, R, Ruby, Tcl, C#, Java, and GNU Octave.

The application Bio7 is an integrated development environment for ecological modelling and contains powerful tools for model creation, scientific image analysis and statistical analysis. The application itself is based on an RCP-Eclipse-Environment (Rich-Client-Platform) which offers a huge flexibility in configuration and extensibility because of its plug-in structure and the possibility of customization.

CyberUnits is a cross-platform class library for rapid development of high-performance computer simulations in life sciences. It supports modelling for biomedical cybernetics and systems biology with Object Pascal.

TEACUP automates many aspects of running TCP performance experiments in a specially-constructed physical testbed. TEACUP enables repeatable testing of different TCP algorithms over a range of emulated network path conditions, bottleneck rate limits and bottleneck queuing disciplines. TEACUP utilises a text-based configuration file to define experiments as combinations of parameters specifying desired network path and end host conditions. When multiple values are provided (e.g. for TCP congestion control algorithm), an experiment is made up of multiple tests. For each experiment and test, TEACUP collects a range of data, such as tcpdump files of traffic seen or TCP stack information (e.g. using Web10G). TEACUP also collects a variety of metadata from the end hosts and bottleneck router, such as the actual OS/kernel version(s) used. TEACUP also provides some simple tools for analysing the results of experiments, such as plotting a flow's experienced RTT over time.

VLE is a multi-modeling and simulation environment to study complex dynamic systems. VLE is based on the discrete event specification DEVS. and it implements the DSDE formalism (A merge of Dynamic Structure DEVS, DSDEVS, with Parallel DEVS, PDEVS). VLE provides a complete set of C++ libraries, called VFL (VLE Foundation Libraries), to develop DEVS models, to gets results of simulations, to launch simulation on cluster. The models can be developed with the DEVS formalism or with the classical mathematical formalism: Ordinary Differential Equation with Euler, Range-Kutta or QSS integrator, Finite state automaton (FDDEVS, UML State chart, Hybrid Petri net). The VLE environment provides an IDE to develop C++ models, DEVS coupled models. VLE have also three ports to use the VFL with Python, Java and R programming languages.

Built with R, this code allows the user to simulate the game of basketball. While the main focus is on simulating the NBA, the code provides a framework for simulating other leagues by modifying the default set of rules.

A simple ICA model for UK non life insurance

GUI for the ISOP R module. The ISOP R module is an implementation of the equal named IRT model by Scheiblechner

IMPORTANT: ========== Our project home page: https://openmpp.org Our latest source code and release available at: https://github.com/openmpp/main/releases/latest Our documentation available at: https://github.com/openmpp/openmpp.github.io/wiki SourceForge files still exist for historical reason. OpenM++ is an open source microsimulation platform inspired by and compatible with Modgen. OpenM++, compared to its closed source predecessor Modgen, has advantages like portability, scalability and open source. It is not a copy of Modgen, but a new, functionally equivalent implementation of the publicly available language specification.

A set of R functions (www.r-project.org) for simple hydrological models, based on linear and non-linear storages, MC simulation and multi-objective evaluation. The code provided here is aimed to be transformed into a proper R package at some point.

SimAssem is a tool for investigating the performances of species richness estimators across widely ranging assemblages and survey designs. Assemblages are simulated by specifying the: 1) number of species, 2) total abundance of individuals, 3) abundance distribution, 4) spatial configuration, 5) species detection probabilities, 6) survey design, and 7) number of grid cells to survey (out of a square grid comprised of 10,000 cells). SimAssem also processes existing encounter data and formats existing and simulated encounter data for other programs. SimAssem, along with the open source code, will hopefully give researchers and managers a tool for assessing the development of new estimators and evaluating potential decisions in a simulated environment. More information on SimAssem is given in Chapter 2, 'SimAssem: a program for simulating species assemblages and estimating species richness', in the dissertation 'Simulating species assemblages and evaluating species richness estimators'.

WATS is an R package (>R2.15) whose main purpose is to run an individual-based simulation of an acoustically-assisted mark-recapture survey of whales Version 1.0 = for R2.15.x Version 1.01 = for R3.0.3+ For details see: Peel D, Miller BS, Kelly N, Dawson S, Slooten E and Double MC (2014) A simulation study of acoustic-assisted tracking of whales for mark-recapture surveys. PloSOne in press.

YANG (Yet Another Network Generator - Java) enables you to generate social networks given various social rules observed in the real population. Uses: generate realistic networks to be used in individual-centric models, teaching or benchmarking.

Program that compares the time of execution between a Neural Network and the formula of distance.

Tool to simulate pressure-driven liquid filtration by filters composed by channels with nanostructured walls.

This package provides high-level interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior.