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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.
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Nice implementation of connectivity measures with increase speed + validation.
I have no concerns given that you obtain the same results in the validation phase. I a little surprised that you get 'ide…
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i mean, check it out: it's got clear BOUNDARIES
```
D = io.quickLoadByDate('20120601');
Y = D.blocks(2).latents;
NB = D.blocks(2).fDecoder.NulM2;
[u,s,v] = svd(D.blocks(2).latents*NB);
Y2 = Y*NB*v;
…
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To improve signal-to-noise, remove baseline, possible sources of spurious correlations, and task structure: spatial smoothing using a gaussian kernel with three voxels
Here are useful link to do kern…
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```
public static double SmoothingRule(double[] observations)
{
return (observations.StandardDeviation(true) * Math.Pow(4.0 / (3.0 * observations.Length), -0.2));
}
I'm getting a value of 38.57 w…
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As far as I know, calculate the probability density function(pdf) using kernel density estimation(kde) does not need a training procedure.
The syntax is like this: pdf_agf [-n] [-k k] [-W Wc] train t…
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```
public static double SmoothingRule(double[] observations)
{
return (observations.StandardDeviation(true) * Math.Pow(4.0 / (3.0 * observations.Length), -0.2));
}
I'm getting a value of 38.57 w…