As the estimation requires MCMC, `sspse`

can take
advantage of multiple CPUs or CPU cores on the system on which it runs, as
well as computing clusters. It uses package `parallel`

and `snow`

to facilitate this, and supports MPI cluster type and likely PSOCK.

1 2 |

`parallel` |
scale; Number of threads in which to run the sampling. Defaults to 1 (no parallelism). |

`type` |
API to use for parallel processing. Supported values are |

`seed` |
integer; random number integer seed. Defaults to |

`packagenames` |
Names of packages in which load to get the package to run functions in addition to those autodetected. This argument should not be needed outside of very strange setups. |

`verbose` |
logical; if this is |

The number of nodes used and the parallel API are controlled using the
`parallel`

and `type`

arguments.

The `parallel`

package is used with PSOCK
clusters by default, to utilize multiple cores on a system. The number of
cores on a system can be determined with the `detectCores`

function.

This method works with the base installation of R on all platforms, and does not require additional software.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
## Not run:
# Uses 2 SOCK clusters for MCMLE estimation
N0 <- 200
n <- 100
K <- 10
# Create probabilities for a Waring distribution
# with scaling parameter 3 and mean 5, but truncated at K=10.
probs <- c(0.33333333,0.19047619,0.11904762,0.07936508,0.05555556,
0.04040404,0.03030303,0.02331002,0.01831502,0.01465201)
probs <- probs / sum(probs)
# Look at the degree distribution for the prior
# Plot these if you want
# plot(x=1:K,y=probs,type="l")
# points(x=1:K,y=probs)
#
# Create a sample
#
set.seed(1)
pop<-sample(1:K, size=N0, replace = TRUE, prob = probs)
s<-sample(pop, size=n, replace = FALSE, prob = pop)
out <- posteriorsize(s=s,interval=10,parallel=2)
plot(out, HPD.level=0.9,data=pop[s])
summary(out, HPD.level=0.9)
# Let's look at some MCMC diagnostics
plot(out, HPD.level=0.9,mcmc=TRUE)
## End(Not run)
``` |

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