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add_to_tid environment variable in run_on_cluster().summarize() function to calculate Monte Carlo standard errors and confidence intervals for all inferential summary statistics. For summary statistics meant to estimate a population quantity (e.g., the bias of an estimator), the Monte Carlo standard error quantifies variability due to running a finite number of simulation replicates.batch() function, which allows for sharing of data or objects between simulation replicates. Essentially, it allows you to take your simulation replicates and divide them into “batches”; all replicates in a given batch will then share a single set of objects. The most common use case for this is if you have a simulation that involves generating one dataset, analyzing it using multiple methods, and then repeating this a number of times. See the documentation for more info.summarize() function and added several summary metrics (correlation, covariance, n_reps); see the documentation for more info.run_on_cluster().sessionInfo() to vars().sim_id to rep_id to avoid confusion with sim_uid.sim_uids.keep_extra option from update_sim() and update_sim_on_cluster()..add option from set_levels().set_levels() to prevent changing of level variables once they are initially set.add_creator(), add_method(), add_constants(). Instead, functions declared in the parent frame of the new_sim() call are automatically added to the simulation object. It is now recommended that simulation constants are stored and referenced in the same way as levels; see “Advanced Usage” on the SimEngine website (https://avi-kenny.github.io/SimEngine/).add_method().set_config() (since this was a source of confusion among some users).These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.