The total number of observations (i.e. nrow(data)).

The function used to compute individual loo contributions.

See llfun in loo.function().

For the loo.function() method and the loo_i() function, these are the data, posterior draws, and other arguments to pass to the log-likelihood function. See the Methods (by class) section below for details on how to specify these arguments.

For the loo.function() method and the loo_i() function, these are the data, posterior draws, and other arguments to pass to the log-likelihood function. See the Methods (by class) section below for details on how to specify these arguments.

Vector of relative effective sample size estimates for the likelihood (exp(log_lik)) of each observation. This is related to the relative efficiency of estimating the normalizing term in self-normalizing importance sampling when using posterior draws obtained with MCMC. If MCMC draws are used and r_eff is not provided then the reported PSIS effective sample sizes and Monte Carlo error estimates will be over-optimistic. If the posterior draws are independent then r_eff=1 and can be omitted. See the relative_eff() helper functions for computing r_eff.

Should the "psis" object created internally by loo() be saved in the returned object? The loo() function calls psis() internally but by default discards the (potentially large) "psis" object after using it to compute the LOO-CV summaries. Setting save_psis=TRUE will add a psis_object component to the list returned by loo. Currently this is only needed if you plan to use the E_loo() function to compute weighted expectations after running loo.

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 loo.cores is removed in a future release. 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.

• Note for Windows 10 users: it is strongly recommended to avoid using the .Rprofile file to set mc.cores (using the cores argument or setting mc.cores interactively or in a script is fine).

For the loo.function() method and the loo_i() function, these are the data, posterior draws, and other arguments to pass to the log-likelihood function. See the Methods (by class) section below for details on how to specify these arguments.

See is_method for loo()