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Currently this package only supports synthesis of causal effect estimates. We could extend to support synthesis of incidence (and prevalence) rates (or proportions).
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### Description
Credible intervals can be specified as equal-tailed intervals (ETIs) or highest density intervals (HDIs). It would be great to add option to choose between ETIs and HDIs in JASP des…
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Let me know if you agree!
Currently `precisions` is an argument in `xpsi.PostProcessing.CornerPlotter.plot` that allows you to change the precision of the credible intervals of each parameter. Howe…
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pgmuvi should create an output table so that people can understand what the results are and what they mean.
This might involve two separate implementations for MAP and MCMC solutions, as credible …
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I wanted a way to efficiently compare multiple marginal posteriors in PyMC3/ArviZ like in **Figure 9.10** from Kruschke's book:
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**Is your feature request related to a problem? Please describe.**
[Inverse CDFs](https://en.wikipedia.org/wiki/Quantile_function) are useful for calculating credible intervals for a given distributi…
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Both functions have significant portions of code that are common, they should be abstracted into a single source to reduce duplication.
Extra comment/bit of a side note: One part where they do diff…
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Function to return common summaries of all parameters from the output, e.g. ML, MAP, posterior median, credible intervals etc. Think about if/how these should also be part of the standard output.
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In correlation_test for instance, you use quantiles `q = quantile(ts,[0.005,0.025,0.5,0.975,0.95])` to extract what I think is credible intervals. Is there a reason to use quantiles instead of HDI?