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I see that h2o implements Naive Bayes. Nevertheless, it only assumes gaussian distribution for contiuous covariates. The klaR R package implements kernel density estimation for continuous covariate th…
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I'd like to add KDE (kernel density estimation) functionality for the 1D and 2D histogram plotting functions, `hist`, `hist2d`, and maybe `hexbin`. Users can then optionally add marginal distribution …
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Would it be possible to implement the plug-in method (rule of thumb), that is used in bandwidth estimation for densities, in the function "npregbw" as well?
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(As mentioned in a few places: #948, #943, #791.)
We can look at https://github.com/uwdata/fast-kde/ for speed, and https://observablehq.com/@d3/kernel-density-estimation for a more straightforward…
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Thanks for the effort. I was checking the lens/projection functions, I could not find the functions that are used in Singh original paper such as gaussian density or eccentricity. May I ask if these a…
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Doing kernel densities using the epanechnikov kernel is very common, but does not seem to be a built-in option. Perhaps because epanechnikov is not included in Distributions.jl.
I think it would be …
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aside: pygam has penalized Expectile regression according to the examples/documentation
Currently RLM and QuantileRegression only use a IRLS algorithm
For RLM we can add gradient fit with scipy …
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Would there be any easy way to implement doing Kernel density estimation on transforms of variables? For example, I have a variable that is constrained to be between 0 and 1, it would be better for me…
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More pictorial representation required to understand which feature is more important in predicting the survival.
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Pick and implement reasonable algorithm for a default kernel density estimate choice.