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Simulate from ODE-Based Models

Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.

Reliably Return the Source and Call Location of a Command

Robust and reliable functions to return informative outputs to console with the run or source location of a command. This can be from the 'RScript'/R terminal commands or 'RStudio' console, source editor, 'Rmarkdown' document and a Shiny application.

Weighted Portmanteau Tests for Time Series Goodness-of-fit

This packages contains the Weighted Portmanteau Tests as described in "New Weighted Portmanteau Statistics for Time Series Goodness-of-Fit Testing' accepted for publication by the Journal of the American Statistical Association.

A C++ Framework for Plant-Plant Interaction IBMs

A tool for simulating a variety of spatial individual-based models of
plant-plant interactions. User-created models can include any number of species, each of
which can be structured in any number of life-stages, where each life-stage has specific
death, growth and reproduction rates, as well as specific interaction radius, dispersal
radius, and interaction effects over each other species/life-stage. Life stages were modeled
so as to be a stochastic, individual-based version of differential Matrix Population
Models (Caswell 2001, ISBN:0-87893-096-5). Interactions can be positive
(facilitation) or negative (competition) and can affect death rates, growth rates or
reproduction rates. Interactions from multiple numbers are additive, so as to best
approximate classic population dynamics models such as the logistic model and
Lotka-Volterra model (Britton 2004, ISBN:9781852335366). All models work in continuous time, implemented as an optimized version
of the Gillespie algorithm (Gillespie 1976

Tools for Modelling of Animal Flight Performance

Allows estimation and modelling of flight costs in animal (vertebrate) flight,
implementing the aerodynamic power model described in Klein Heerenbrink et al.
(2015)

Stochastic Hybrid Models in Dynamic Networks

Simulates stochastic hybrid models for transmission of infectious
diseases in dynamic networks. It is a metapopulation model in which each
node in the network is a sub-population and disease spreads within nodes
and among them, combining two approaches: stochastic simulation algorithm
or its approximations (Gillespie DT (2007)

Parsing Expression Grammars in Rcpp

A wrapper around the 'Parsing Expression Grammar Template Library', a C++11 library for generating Parsing Expression Grammars, that makes it accessible within Rcpp. With this, developers can implement their own grammars and easily expose them in R packages.

Data Structures, Summaries, and Visualisations for Missing Data

Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. 'naniar' provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of 'ggplot2' and tidy data.

sbioPN: Simulation of deterministic and stochastic spatial biochemical reaction networks using Petri Nets

sbioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction networks with spatial effects. Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions are defined. For deterministic solutions, sbioPN creates the associated system of differential equations "on the fly", and solves it with a Runge Kutta Dormand Prince 45 explicit algorithm. For stochastic solutions, sbioPN offers two variants of Gillespie algorithm, or SSA. For hybrid deterministic/stochastic, it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions. sbioPN algorithms are developed in C to achieve adequate performance.

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.