statistical | tiny easy-to-use bash script | Analytics library

This could be one way to achieve your task:

Resulting plot:

The code

Turning @DNF's comment into an answer:

You can use the cdf function from Distributions.jl:

The cause for your code failing is that you end up performing a Map.get with Integer key over a Map which during groupingBy was constructed with Strings in keyset. Hence the corresponding null value in lookup followed by an NPE.

It would be simpler if you could think of formulating your requirements into two steps. One, where you summarise the data that is in your hand. Other, where you map it to the desired result object of your choice.

With the help of existing Collectors.summarizing..., this could look as simple as the following:

The key elemnent is tableGrob() from gridExtra package!

We could use grid.arrange().

For the table use tableGrob() to create a table like the plot of a data frame. Then you can use it with grid.arrange() function.

You don't need a custom template. Just like you do in your tex document, you can make the desired configurations in the header of your rmarkdown document:

You already know how to make the author names bold from biblatex: customizing bibliography entry - the same technique can be used for the editorstrg:

In my experience, these errors on Unix often stem from missing external libraries. For example, installing the R xml2 package requires libxml2-dev to be installed via the system package manager (i.e. outside R) otherwise installation will fail.

I can't read French, but it looks to me as though the dependency jpeg failed, due to a missing external jpeg library, and then everything cascaded from there. You could try installing some version of the libjpeg library. I know it comes pre-installed in Ubuntu which may be why that worked for you. I'm a little surprised it doesn't come installed already in OpenSUSE, but I have no experience with OpenSUSE.

I don't know if you can set a condition to OLS such that the absolute value of the coefficients are all less than a constant.

Regularization is a good alternative to this kind of problem though. Basically, L1 or L2 regularization penalize the sum of the coefficients in the optimization function, which pushes the coefficients of the least significant variables close to zero so they don't raise the value of the cost function.

Take a look at lasso, ridge and elastic net regression. They use L1, L2 and both forms of regularization respectively.

You can try the following in statsmodels:

This is really an abuse of pandas, but if you insist, the following will produce the result you desire: