Simulation-Based Landscape Construction for Dynamical Systems
Create and modify argument sets, then make an argument grid for batch ...
Convert a list of simulation output to a mcmc.list object
as.mcmc.list generic function
Attach all matrices in a batch simulation
Get a ggplot2 layer from a barrier object
Autolayer generic function
A simple gradient simulation function for testing
Simulate multiple 1-3D Markovian Stochastic Differential Equations
Pipe operator
Make plots from landscape objects
Save landscape plots
Perform a batch simulation.
Functions for calculating energy barrier from landscapes
Graphical diagnoses to check if the simulation converges
Fill a vector of values into a structure list.
Summarize the barrier height from a barrier
object
Get the probability distribution from a landscape object
Get a ggplot2 layer from a barrier object
Class "hash_big_matrix": big matrix with a md5 hash reference
Make a matrix of 2D static landscape plots for one or two parameters
Make 2D static landscape plot for a single simulation output
Make a tidy data.frame
from smooth 2d distribution matrix
Make 3d animations from multiple simulations
Make a matrix of 3D static landscape plots for one or two parameters
Make 3D static landscape plots from simulation output
Make 4D static space-color plots from simulation output
Make a grid for calculating barriers for 2d landscapes
Make a grid for calculating barriers for 3d landscapes
Calculate 1D, 2D, or 3D kernel smooth distribution
Modify a single simulation
A simple non-gradient simulation function for testing
A simple simulation function for testing
A simple yet meaningful simulation function for testing
Simulate 1-3D Markovian Stochastic Differential Equations
simlandr: Simulation-Based Landscape Construction for Dynamical System...
Summarize the barrier height from a barrier
object
A toolbox for constructing potential landscapes for dynamical systems using Monte Carlo simulation. The method is based on the potential landscape definition by Wang et al. (2008) <doi:10.1073/pnas.0800579105> (also see Zhou & Li, 2016 <doi:10.1063/1.4943096> for further mathematical discussions) and can be used for a large variety of models.
Useful links