As of version `2.0.0`

this function is **deprecated**. Please use the
`psis()`

function for the new PSIS algorithm.

psislw(
lw,
wcp = 0.2,
wtrunc = 3/4,
cores = getOption("mc.cores", 1),
llfun = NULL,
llargs = NULL,
...
)

## Arguments

lw |
A matrix or vector of log weights. For computing LOO, `lw = -log_lik` , the *negative* of an \(S\) (simulations) by \(N\) (data
points) pointwise log-likelihood matrix. |

wcp |
The proportion of importance weights to use for the generalized
Pareto fit. The `100*wcp` \
from which to estimate the parameters of the generalized Pareto
distribution. |

wtrunc |
For truncating very large weights to \(S\)^`wtrunc` . Set
to zero for no truncation. |

cores |
The number of cores to use for parallelization. This defaults to
the option `mc.cores` which can be set for an entire R session by
`options(mc.cores = NUMBER)` , the old option `loo.cores` is now
deprecated but will be given precedence over `mc.cores` until it is
removed. **As of version 2.0.0, the default is now 1 core if
**`mc.cores` is not set, but we recommend using as many (or close to as
many) cores as possible. |

llfun, llargs |
See `loo.function()` . |

... |
Ignored when `psislw()` is called directly. The `...` is
only used internally when `psislw()` is called by the `loo()`
function. |

## Value

A named list with components `lw_smooth`

(modified log weights) and
`pareto_k`

(estimated generalized Pareto shape parameter(s) k).

## References

Vehtari, A., Gelman, A., and Gabry, J. (2017a). Practical Bayesian model
evaluation using leave-one-out cross-validation and WAIC.
*Statistics and Computing*. 27(5), 1413--1432. doi:10.1007/s11222-016-9696-4
(journal version,
preprint arXiv:1507.04544).

Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2019).
Pareto smoothed importance sampling.
preprint arXiv:1507.02646

## See also