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#package

8 posts7 participants1 post today

I am wondering if there are recently any packages missing in FreeBSD latest repo? Yesterday my system complained that it had no Golang installed and when I tried to run pkg install go, I got "No packages available to install matching 'go'..." Same with other packages like yazi. Both should be in the repo.

I am wondering if anyone has information on why these packages are not available. At least go is quite common and it's really strange why it's not in the repo. Or is it because of my config??

Handling missing data is a critical step in data analysis, as failing to address it properly can lead to biased results and reduced analytical power. The mice package for R, short for Multivariate Imputation by Chained Equations, provides a robust and flexible framework for handling missing values through multiple imputation.

The visualizations shown below originate from the package website: github.com/amices/mice

More info: eepurl.com/gH6myT

Basic boxplots are often not the best way to visualize your data! They can hide important information, such as the distribution of individual data points or group-specific differences.

The attached visual showcases several ways to enhance boxplots.

All of these examples were created using ggplot2 and extensions in R.

Click this link for detailed information: statisticsglobe.com/online-cou

🎩✨Oh, joy! Another "luxurious" #package #manager for #Lua. Because, obviously, what Lua really needed was a glorified #cargo #cosplay to solve the earth-shattering problem of... #wrong headers? 😆 Imagine the screams of excitement from the, what, 12 people who noticed!
mrcjkb.dev/posts/2025-04-07-lu #Luxurious #Headers #Developer #Humor #HackerNews #ngated

mrcjkb.devmrcjkb.dev - Announcing Lux - a luxurious package manager for Lua

ggplot2 is the gold standard when it comes to data visualization.

The image in this post showcases examples of ggplot2 visualizations, demonstrating its versatility to create a wide range of plots with nearly limitless customization options.

Check out my online course, "Data Visualization in R Using ggplot2 & Friends," for a deeper dive into creating stunning plots with ggplot2.

More info: statisticsglobe.com/online-cou

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I'm trying to make a _good_ .deb package.

Yet I'm having a hard time finding a blog post where someone explain their experience going from bare minimum to a fairly advanced package with multiple stuff going pre and post install.

By _good_ I also mean something that helps understanding the why some packager use /etc/default/xyz and /etc/xyz/ yet some never use /etc/default .

Any pointers ?

@mrkaran Great insights & a pleasure to read. I have used #nixos as my main machine for a few years & its main advantage is that it won't let me mess it up because I do not install anything other than via the configuration.nix. Your post has given me a view on how to use it properly.
My big question: how can one #package something to get it into the nixOS search.nixos.org/packages
? I am happy to do the work but no micro step by step guide seems available. I am thinking of #reticulum #meshchat

search.nixos.orgNixOS Search

If you're a MATLAB user, you should consider switching to R programming now!

Transitioning from MATLAB to R can significantly enhance your data analysis capabilities and open up new opportunities for professional growth.

Please note: MATLAB has strengths in advanced numerical analysis and simulations. While R is excellent for statistics and data analysis, MATLAB excels in real-time simulations, process control, and complex differential equations.

Details: statisticsglobe.com/online-cou

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#statstab #304 {MRCV} Methods for Analyzing Multiple Response Categorical Variables (MRCVs)

Thoughts: These are some of the trickier types of data, but maybe not hopeless for analysis.

#mrcv #analysis #r #package #guide #tutorial #categorical

cran.r-project.org/web/package

cran.r-project.orgMRCV: Methods for Analyzing Multiple Response Categorical Variables (MRCVs)Provides functions for analyzing the association between one single response categorical variable (SRCV) and one multiple response categorical variable (MRCV), or between two or three MRCVs. A modified Pearson chi-square statistic can be used to test for marginal independence for the one or two MRCV case, or a more general loglinear modeling approach can be used to examine various other structures of association for the two or three MRCV case. Bootstrap- and asymptotic-based standardized residuals and model-predicted odds ratios are available, in addition to other descriptive information. Statisical methods implemented are described in Bilder et al. (2000) <<a href="https://doi.org/10.1080%2F03610910008813665" target="_top">doi:10.1080/03610910008813665</a>>, Bilder and Loughin (2004) <<a href="https://doi.org/10.1111%2Fj.0006-341X.2004.00147.x" target="_top">doi:10.1111/j.0006-341X.2004.00147.x</a>>, Bilder and Loughin (2007) <<a href="https://doi.org/10.1080%2F03610920600974419" target="_top">doi:10.1080/03610920600974419</a>>, and Koziol and Bilder (2014) <<a href="https://journal.r-project.org/articles/RJ-2014-014/" target="_top">https://journal.r-project.org/articles/RJ-2014-014/</a>>.

🎉 Wow! Who knew the secret to speeding up #Ubuntu packages was just to rebuild them? 🚀 Next time someone tells you to run "apt-get update," just roll your eyes and say, "Sure, but have you tried turning it off and on again?" 🙄
gist.github.com/jwbee/7e8b27e2 #Speedup #Package #Rebuilds #AptGetUpdate #TechHacks #Humor #HackerNews #ngated

Make Ubuntu packages 90% faster by rebuilding them - jq.md
GistMake Ubuntu packages 90% faster by rebuilding themMake Ubuntu packages 90% faster by rebuilding them - jq.md