Author: matloff

Several updates. All packages are on CRAN, but please use GitHub for the latest.continue reading.

I’ve added a new function to qeML 1.2, qeMittalGraph, based on an idea by my student Aditya Mittal. Below is an example that I think is rather compelling. The basic...continue reading.

I’m very pleased to announce a new package, dsld, available on CRAN. This is the work of eight talented undergrad students. I provided the concept and some general guidance, but...continue reading.

Readers who are interested in the Data Privacy field may find our new paper (Perry, Matloff, Tendick) of interest, https://tdp.cat/issues21/tdp.a478a22.pdf…. There we introduce a new method that we call RWN,...continue reading.

The famed physicist Richard Feynman once said, “I learned very early the difference between knowing the name of something and knowing something,” a lesson from his father. I think too...continue reading.

I’ve added a new function, qeNeuralTorch, to the qeML package, as an alternative to the package’s qeNeural. It is experimental as this point, but usable and I urge everyone to...continue reading.

In my December 22 blog, I first introduced the classic parametric quantile regression (QR) concept. I then showed how one could use the qeML package to perform quantile regression nonparametrically, using...continue reading.

In this post, I will first introduce the concept of quantile regression (QR), a powerful technique that is rarely taught in stat courses. I’ll give an example from the quantreg...continue reading.

Is machine learning overrated, with traditional methods being underrated these days? Yes, ML has had some celebrated successes, but these have come after huge amounts of effort, and it’s possible...continue reading.

The data.table 2023 user community survey is here, open until December 1st.continue reading.

In writing an R package, it is often useful to build up some function call in string form, then “execute” the string. To give a really simple example: Quite a...continue reading.

What about variable selection? Which predictor variables/features should we use? No matter what anyone tells you, this is an unsolved problem. But there are lots of useful methods. See the...continue reading.

Sorry I haven’t been very active on this blog lately, but now that I have more time, that will change. I’ve got myriad things to say. To begin with, then,...continue reading.

I’ve recently completed fastStat, https://github.com/matloff/fastStat,a quick introduction to statistics for those who’ve had a calculus-based probability course. Many such people later need to do statistics, and this will give them...continue reading.

Many of you may have heard of ChatGPT, a dazzling new AI tool. We are hearing lots of gushing praise for the tool. Well, how well does it do in...continue reading.

The field of data privacy has long been of broad interest. In a medical database, for instance, how can administrators enable statistical analysis by medical researchers, while at the same...continue reading.

I have a new short writeup, showing common R design patterns, implemented side-by-side in base-R and Tidy. As readers of this blog know, I strongly believe that Tidy is a...continue reading.

During the last year or so, I’ve been quite interested in the issue of fairness in machine learning. This area is more personal for me, as it is the confluence...continue reading.

George Ostrouchov, one of R’s top parallel computing experts, will run a workshop on cluster parallel computing in R next week. BTW, even a multicore laptop is a “cluster,” so...continue reading.

As many readers of this blog know, I strongly believe that R learners should be taught base-R, not the tidyverse. Eventually the students may settle on using a mix of...continue reading.