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- **I'm submitting a ...**
[ ] bug report
[ ] feature request
[ ] question about the decisions made in the repository
[ x] question about how to use this project
- **Summary**
Hi.
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### Description
New robust statistical procedures as contained in the WRS2, bmtest GRD & flipscores Packages
### Purpose
Incorporate widely used robust statistical procedures
### Use-case
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Surbhi and I plan to spend more time explaining the theory of NNs with more focus on:
- Datasets. Dimensions.
- An expanded introduction:
- Potential applications. What NNs can do...
- Other a…
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**An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)**
**An Introduction to Statistical Learning** provides an accessible overview of the field of statis…
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https://www.wiley.com/en-ae/R+Programming+for+Actuarial+Science-p-9781119754992
R Programming for Actuarial Science
Professional resource providing an introduction to R coding for actuarial and fi…
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This task is to plan and track progress towards a baseline library of useful graphs. It also provides an opportunity to unify and plan chart components, so they are shared throughout the library.
# Ch…
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**Augmented Random Forest with Kernel Convolution**
For fast prototyping, a smooth and flexible representation of functions is essential. Traditional approaches using trees or forests for function …
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As discussed with @siddharthteotia, consider adding some common statistical analysis methods SQL language.
Few examples:
1. Pearson's coefficient
2. Sampling (bernoulli/stratified)
5. Histogram…
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Dear Bruno,
Me again concerning the `denoising.py` example that I've run asis.
Below I have put the results where I have added the SNR defined as
```python
def snr(x,x_ref):
return 10*np.lo…