Description of your problem

Most distributions support multiple parameterizations that are hard to grasp from the little description that we have. In some cases the scipy.stats documentation is not helping either.

Proposal / Ideas for improvement

• state what the default parameterization would be
• show examples with both (equal) parameterizations
• link to the scipy.stats distrib

Expected behavior and actual behavior.

Right now there are some statistics on the current or a specific branch, I couldn't find a way to output per user commits for instance of all branches. Would be nice to have.

Steps to reproduce the problem.

Specifications like the version of the project, operating system, or hardware.

Hello.
I've come across what (to me) seems to be a problem with the FILENAME and FILENUM variables.

# mlr --version
Miller v5.6.2

# cat /tmp/csv1
A,B,C
_2GB,255,2
_4GB,120,4
_6GB,50,6
_10GB,10,10

# cat /tmp/csv2
FIRST,SECOND,THIRD,FOURTH
1,2,3,4
5,6,7,8
9,10,11,12
13,14,15,16

# mlr --icsv cat then put 'print FILENAME'   /tmp/csv1 /tmp/csv2
/tmp/csv1
A=_2GB,B=255,C=2
/

Use mvn release:prepare to build the docs and copy them to the /docs directory

When trying to show any of the listed example outputs in the Readme.md, I'm forwarded to a weird site:

Example link:

• http://githubproxy.ejwa.se/wiki/ejwa/gitinspector/examples/django_output.html

Description

Recently I found that normalized_table_calc function is too slow (more than 30% of the total execution time!!!) and it seems that the reason is the poor performance of numpy.around and built-in round function in scalar mode!!

I think, we should define our custom rounder function, something like this :

def custom_rounder(input_number,digit):
    p =

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

Could you please add examples on how to query/select? E.g. how does a pandas query like below look in DataFrame?

df_filtered = df[df.something > 5]

1. Add sample size estimation documentation.
2. Improve multiple correction documentation adding all formulas and assumptions.

feel free to add more

https://en.wikipedia.org/wiki/Local_outlier_factor

See HdrHistogram/HdrHistogram_rust#74 (comment) for more context.

We save in fields (and write when serializing) the requested limits, not the actual limits that result from the underlying encoding (which will encompass at least as much as what the user requested, and maybe more). Perhaps we should expose the actual limits of what a particular histogram can do, rather th

In order to count methylated and unmethylated cystosines, the documentation at 3.5 Tiling windows analysis states this code:

tiles=tileMethylCounts(myobj,win.size=1000,step.size=1000)

In order to obtain the myobj object this code is stated further up (which is the only place where this is defined):

# read the files to a methylRawList object: myobj
myobj=methRead(file.l