These are saved results from toy examples used in other documentation page for the package. It gives estimates by simulation of log-likelihoods of the `(mu,s2)`

parameters of a Gaussian distribution for a given sample of size 20 with mean 4.1416238 and (bias-corrected) variance 0.9460778. `densv`

is based on the sample mean and sample variance as summary statistics, and `densb`

on more contrived summary statistics.

1 2 |

Data frames (with additional attributes) with observations on the following 5 variables.

`mu`

a numeric vector; mean parameter of simulated Gaussian samples

`s2`

a numeric vector; variance parameter of simulated Gaussian samples

`sample.size`

a numeric vector; size of simulated Gaussian samples

`logL`

a numeric vector; log probability density of a given statistic vector inferred from simulated values for the given parameters

`isValid`

a boolean vector. See

`infer_logLs`

for its meaning.

Both data frames are return objects of a call to `infer_logLs`

, and as such they includes attributes providing information about the parameter names and statistics names (not detailed here).

See step (3) of the workflow in the Example on the main `Infusion`

documentation page, showing how `densv`

was produced, and the Example in `project`

showing how `densb`

was produced.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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