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aosmith.rbind.io | ||
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www.rdatagen.net
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| | | | | Simulation can be super helpful for estimating power or sample size requirements when the study design is complex. This approach has some advantages over an analytic one (i.e.one based on a formula), particularly the flexibility it affords in setting up the specific assumptions in the planned study, such as time trends, patterns of missingness, or effects of different levels of clustering. A downside is certainly the complexity of writing the code as well as the computation time, which can be a bit painful. My goal here is to show that at least writing the code need not be overwhelming. | |
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www.fromthebottomoftheheap.net
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www.ericekholm.com
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| | | | | Learning maximum likelihood estimation by fitting logistic regression 'by hand' (sort of) | |
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voipsa.org
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| | | Given recent announcements of security fixes in Asterisk, it was great to see: 1) Kevin Fleming from Digium posting Asterisk security advisories to the VOIPSEC mailing list; and 2) the creation of ... | ||
