dsdive.obstx.matrix: Compute probability transition matrix for partially observed...
In jmhewitt/dsdive: Discrete Space Models for Dive Trajectories of Animals

Given model parameters, this function will compute the probability transition matrix for a Continuous time Markov chain (CTMC) that is observed once, and then again at a time of tstep units of time later. If include.raw==TRUE, then raw components of the probability transition matrix will be returned so that the transition matrix can be computed for arbitrary timesteps.

dsdive.obstx.matrix(
  depth.bins,
  beta,
  lambda,
  s0,
  tstep,
  include.raw = FALSE,
  delta
)

`depth.bins`	n x 2 Matrix that defines the depth bins. The first column defines the depth at the center of each depth bin, and the second column defines the half-width of each bin.
`beta`	the current depth bin transition model parameters
`lambda`	the current depth bin transition rate model parameters
`s0`	the stage for which to compute the transition matrix
`tstep`	Time between observations of the CTMC
`include.raw`	`TRUE` to include raw components of the probability transition matrix so that the matrix can be computed for arbitrary timesteps
`delta`	If `delta>0`, then the observation matrix and raw components computed will be for a transition matrix whose generator is perturbed to allow much faster computation.

data('dive.sim')
attach(dive.sim)
attach(dive.sim$params)

o = dsdive.obstx.matrix(depth.bins = depth.bins, beta = beta, lambda = lambda, 
                        s0 = 1, tstep = 300, include.raw = TRUE, delta = 1e-10)


detach(dive.sim$params)
detach(dive.sim)