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Just curious - what's the relationship/overlap/complementarity of this package and the uncertainty machinery in https://github.com/JuliaPhysics/Measurements.jl?
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When using bootstrap to evaluate the causal structure given by LiNGAM, multiscale bootstrap is said to be better. Is the bootstrap method already prepared here a multiscale one?
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Would it be possible or sensible to add support for sample weights (at the observation level) to this package? Most scikit-learn estimators allow the user to pass a sample weight into the `.fit()` cal…
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Related to #59 - we were effectively including unnecessary truth comparison variance by accidentally using only one bootstrap sample. While we will proceed w/ #59 for now, ultimately for the final pap…
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Quite useful and visually attractive: http://blog.enthought.com/general/visualizing-uncertainty/
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This outlines a roadmap for basic statistical functionality that Julia needs to offer. It is heavily drawn from the table of contents for MASS.
- [ ] Data processing [DataFrames.jl](https://github.com…
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Bootstrapped synthetic likelihood could prevent the repeated N times of fitting; resampling from one set of recovered parameters with some estimated variance could be used to approximate SBC_vanila's …
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When using permutation_isc and bootstrap_isc with pairwise=False, the permutations are carried out by applying signflips, group exchanges, or bootstrap resampling to the correlation values of individu…
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Calculating survey means and totals is relatively straighforward as compared to their variances in certain designs. For example in the Horvitz Thompson estimator, their is a double inclusion probabili…
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When I use this function `parameters(model, bootstrap = TRUE)` with a binary logistic model estimated using svyglm from the survey package, the bootstrap = TRUE argument does not function as expected…