Need to do some better handling of low-observation models in plot_diagnostics. These are models that shouldn't really be estimated, and we can't really make the plots work, but we shouldn't raise exceptions.

• Any dataset with less than 10 observations will raise an error computing the error autocorrelations:

mod = sm.tsa.statespace.SARIMAX(np.random.normal(size=10), order=(10, 

Hey Contributor!

Thanks for checking out HyperLearn!! Super appreciate it.

Since the package is new (only started like August 27th)..., Issues are the best place to start helping out, and or check out the Projects tab. There's a whole list of stuff I envisioned to complete.

Also, if you have a NEW idea: please post an issue and label it new enhancement.

In terms of priorities, I wanted

For discussion, see IndrajeetPatil/ggstatsplot#333 (comment)

😸

I have programmed for Association Rules using Eclat in both R and Python.

Please help me create a explanatory markdown documentation for the same.

Link for the code is here

The alpha for style = "dotwhisker" has been deprecated. Instead, users can specify conf.level to change the range of the whisker through broom:tidy.

Need to correct the relevant descriptions in the vignette.

dwplot(m1, alpha = 0.01) # not work

dwplot(m1, conf.level = .90)

Check line 249-250 of dwplot.R, though

• model
  • base
  • efs
  • ffx
  • sindy
• utils
• preprocessing