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A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)

Results 53 MultivariateStats.jl issues
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Currently, regression algorithms implemented as stand-alone functions, while other methods use StatsAPI interface, i.e. `fit/predict`. We should have properly derived types from `StatsAPI.RegressionModel` and corresponding implemented interface for various regression...

enhancement
up for grabs

I run the code in the example (bar a minor change as discussed in https://github.com/JuliaStats/MultivariateStats.jl/issues/166 ) and I get a matrix index error: This is the code (till the error)...

Following #95, I looked at MV models/methods implemented in this package, trying to figure out what would be a type hierarchy and corresponding method interfaces for this package. Here is...

Are there any plans on implementing Correspondence Analysis and Multiple Correspondence Analysis?

up for grabs

I'm not sure if this is possible / correct, but it would be nice if there were a unified API for ordinations like MDS and PCA to extract the scaled...

According to the PPCA paper [Tipping, Michael E., and Christopher M. Bishop. "Probabilistic principal component analysis." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61.3 (1999): 611-622.](https://www.microsoft.com/en-us/research/publication/probabilistic-principal-component-analysis/)  one...

I'm wondering whether there's any plan to implement the so-called Varimax Rotation and other rotations of factors or PCA loadings as MATLAB's ```rotatefactors``` function in Statistics Toolbox. Thanks! BVP

up for grabs

Hi, I tried to move some of the R code into Julia, and found the `ridge` regression in `MultivariateStats.jl` to replace `lm.ridge` in R. but it seems that the `lambda`...

Where the idea for code for kernel PCA `reconstruct` in `MultivariateStats.jl`https://github.com/JuliaStats/MultivariateStats.jl/blob/30aea0c2832f5a0a0574641433774eecc57aba92/src/kpca.jl#L61 is taken from? I've found some papers that state that reconstruction in kernel PCA is quite difficult and suggest...

This PR produces the following timings for my `(2, 485156)` testinput ``` 565.305 ms (344 allocations: 37.05 MiB) # baseline 300.948 ms (211 allocations: 37.04 MiB) # inbounds 259.164 ms...