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I am currently working on making the income.py module robust to general ages and ability types S and J. The module currently only works with S=80 and J=7. Because the resulting 80 x 7 ability matrix i…
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- [x] Write the function for naive kernel density estimation
- [x] Write the function for kernel estimation for nonnegative random variables using truncated normal density
- [x] Add comparison between…
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Update the following URL to point to the GitHub repository of
the package you wish to submit to _Bioconductor_
- Repository: https://github.com/hansenlab/yamss
Confirm the following by editing each c…
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Currently the `kde` methods either require the user to provide number of bins, the midpoints or default to 2048. This can be a problem for small datasets. It seems like it would be nice to have a sens…
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Neither the `pm.HalfNormal` distribution nor taking the absolute value of a zero-mean `pm.Normal` distribution seem to produce accurate half-normal distributions:
``` python
import pymc3 as pm
import…
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Hi @dougalsutherland ,
Thanks for sharing your code; it is well documented and well written.
I am working on a problem and comparing different divergences. KL and Hellinger already produce good resu…
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Hi,
I would like to reduce the number of packages we rely on. Do we realy need them all?
Can we transfer some of them to "Suggest"?
For which function do we need "methods" or "grDevices"?
Best
Ulf
ulf85 updated
8 years ago
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instead of fitting multivariate normals, fit some sort of nonparametric density estimator
we want to do this to handle to skew for some of the distributions
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we might not include any of these, but we should definitely look at it before submitting.
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can you first get a null distribution by shuffling observeds to predict observeds?
then compare this to the hypotheses.