## Make transition matrix

### Description

Helper function to (easily) construct sparse transition matrices for ssa.adapativetau.

### Usage

ssa.maketrans(variables, ...)


### Arguments

 variables Either the number of variables in your system OR a vector of strings specifying the names of the variables (in which case the length of this vector is the number of variables). The number of variables equals the number of rows in the returned transition matrix. ... At least one additional argument is *required*. Each should be a matrix with an arbitrary number of columns (each represents a separate transition) and an even number of rows. Going down a particular column, entries alternate between specifying the variable that will change and the amount by which it will change. Variables be specified either by numerical index (1,2,...,numVariables) or character name (if names supplied in the parameter above). See examples below.

### Details

Making large transition matrices can be a real pain, particularly if the entries in the end will be quite sparse. The concept of this function is simple but the easiest way to understand it is probably to check out the examples.

### Value

two-dimensional matrix with rows for each variable and columns for each transition.

### Author(s)

Philip Johnson

You probably only want to use this in preparation for calling ssa.adaptivetau.

### Examples


## trivial Lotka-Volterra example from ssa.adaptivetau
nu = ssa.maketrans(2, #number of variables
rbind(1, +1),
rbind(1, -1, 2, +1),
rbind(2, -1))

## slightly more complicated SIR epidemiological model with two distinct
## susceptible and infected variables (think male and female), birth
## (into S) and death (exclusively from I and R)
nu = ssa.maketrans(c("Sm", "Sf", "Im", "If", "R"), #list of variable names
rbind(c("Sm","Sf"), +1),
rbind(c("Sm","Sf"), -1, c("Im","If"), +1),
rbind(c("Im","If"), -1),
rbind(c("Im","If"), -1, "R", +1),
rbind("R", -1))