Simple simulation of single-cell RNA sequencing data
-
Updated
Dec 10, 2024 - R
Simulation refers to the process of creating a virtual model of a real-world system to study its behavior and performance under various conditions. This topic covers the principles, methodologies, and applications of simulation in fields such as engineering, science, healthcare, and social sciences. Simulations can range from simple models to complex, interactive environments, allowing researchers and practitioners to test hypotheses, train individuals, and predict outcomes without the risks or costs associated with real-world experiments. The topic also explores different types of simulation software and tools, as well as best practices for designing and validating simulations.
Simple simulation of single-cell RNA sequencing data
A particle simulation engine based on a port of d3-force
Code, data and prose of the book: Spatial Microsimulation with R
A statistical framework that serves as a common interface to a large range of models
simstudy: Illuminating research methods through data generation
Contextual Bandits in R - simulation and evaluation of Multi-Armed Bandit Policies
A Toolkit for Using EnergyPlus in R.
Structure for organizing Monte Carlo simulations in R
villager is an extensible agent based modeling (ABM) framework for the R language. It supports agents, agent aggregations and their associated resources, as well as flexible data management.
An R package for stock-assessment simulation with Stock Synthesis
Multi-species size-based ecological modelling in R
Create tidy probability/density tibbles and plots of randomly generated and empirical data.
Latent Variable Models in R https://kkholst.github.io/lava/
Pharmacometric Tools for Modeling & Simulation
Repository of the R-packageGen3sis
Created by The scientific and engineering community