Lewandowski-Kurowicka-Joe (LKJ) distribution is a very useful prior distribution for parameter estimation in correlation matrices, and is also tightly related to matrix factorizations such as Cholesky decomposition. For example, when you use Cholesky decomposition to decompose a variance-covariance matrix (Sigma​) into the multiplication of 3 matrices, you can set LKJCorr​ prior for the correlation matrix R.

Cholesky decomposition of variance-covariance matrices

Its probability density function is defined as:

Column- and row-wise operations

With the development of dplyr or its umbrella package tidyverse, it becomes quite straightforward to perform operations over columns or rows in R. These column- or row-wise methods can also be directly integrated with other dplyr verbs like select, mutate, filter and summarise, making them more comparable with other functions in apply or map families. In this blog, I will briefly cover some useful manipulations over rows or columns.

Example 1: select those string columns with less than 5 levels in the dataset of starwars.

# anonymous function
starwars %>%
select_if(~ is.character(.x) & length(unique(.x)) <= 5)


I am looking for a general-purpose editor that can integrate and customize different features across all programming languages that I often use (e.g., R, Python, Julia and Javascript), and Visual Studio Code seems to be the best candidate for me. Here I will give a quick overview of the key features for deploying my R markdown notebook in VS code. These settings can be easily generalized to other languages by adjusting the engines/compilers or adding the language-specific extensions.

Before I get started, it is necessary to install some extensions.

Simulated data in a Multivariate Normal distribution
Figure 1: Simulated data in a Multivariate Normal distribution

This post provides an example of simulating data in a Multivariate Normal distribution with given parameters, and estimating the parameters based on the simulated data via Cholesky decomposition in stan. Multivariate Normal distribution is a commonly used distribution in various regression models and machine learning tasks. It generalizes the Normal distribution into multidimensional space. Its PDF can be expressed as:

It takes a while for me to figure out how you can preview pictures and pdfs in vifm on Kitty terminal. I tried many different utilities, such as w3m and überzug, but the displayed pictures are not satisfying or it is really a pain to deploy the environments on my MacBook.

I recently find that Kitty can support image display in the terminal via icat function. To make it work, you first need to install ImageMagick. See the Kitty webpage for details, and a tutorial for the configuration of kitty and vifm on your computer. …

A good terminal tool can speed up your workflow, and make your life much easier. Here I give a complete tutorial of installing the kitty terminal, and configuring the fish shell and vifm manager on mac.

  1. kitty

(1) install kitty on mac

>> curl -L https://sw.kovidgoyal.net/kitty/installer.sh | sh /dev/stdin

(2) set kitty as the default terminal tool

To set kitty as the default application, you need to install a plug-in called “RCDefaultApp.prefPane”. You can download it from my github repository.

>> git clone https://github.com/JakeJing/kittyconfig.git
>> sudo mv kittyconfig/RCDefaultApp.prefPane /Library/PreferencePanes/

This will add an icon of “default apps” in your system…

I am trying to deploy the environments for python markdown notebook in Atom, so that you can compile your python script (*.pmd) into a pdf file. This configuration is tailored for markdown lovers and R users, who are looking for a python IDE similar to Rstudio. It is also useful for researchers who want to attach their scripts as well-formed pdfs in the publications. I include the template files, scripts and other settings in my github repository.

  1. install Pweave via pip
>> pip3 install --upgrade Pweave

2. download and install Atom

3. install the necessary packages via Atom package manager

PyTorch has gained great popularity among industrial and scientific projects, and it provides a backend for many other packages or modules. It is also accompanied with very good documentation, tutorials, and conferences. This blog attempts to use PyTorch to fit a simple linear regression via three optimisation algorithms:

  • Stochastic Gradient Descent (SGD)
  • Adam
  • No-U-Turn Sampler (NUTS)

We will start with some simulated data, given certain parameters, such as weights, bias and sigma. The linear regression can be expressed by the following equation:

Jake Jing

Programming, Data analysis & Deep learning!

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