Amazing artworks people made in R:

Multivariate Analysis with Optimal Scaling (Gifi)Implements categorical principal component analysis (‘PRINCALS’), multiple correspondence analysis (‘HOMALS’). It replaces the ‘homals’ package.

Principal components (PC) regression is a common dimensionality reduction technique in supervised learning. The R lab for PC regression in James et al.’s Introduction to Statistical Learning is a popular intro for how to perform PC regression in R: it is on p256-257 of the book (p270-271 of the PDF).

Inverse reinforcement learning is the problem of inferring the reward function of an observed agent, given its policy or behavior. Researchers perceive IRL both as a problem and as a class of methods. By categorically surveying the current literature in IRL, this article serves as a reference for researchers and practitioners in machine learning to understand the challenges of IRL and select the approaches best suited for the problem on hand. The survey formally introduces the IRL problem along with its central challenges which include accurate inference, generalizability, correctness of prior knowledge, and growth in solution complexity with problem size. The article elaborates how the current methods mitigate these challenges. We further discuss the extensions of traditional IRL methods: (i) inaccurate and incomplete perception, (ii) incomplete model, (iii) multiple rewards, and (iv) non-linear reward functions. This discussion concludes with some broad advances in the research area and currently open research questions. A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress

Shiny modules are short (well, usually short) server and UI functions, that can be connected to each other by a common namespace, and be embedded within a regular Shiny app. You can’t run a Shiny module without a parent Shiny app. The modules can contain both inputs and outputs, and are usually centered around a single operation or theme.

Once upon a time, we at STATWORX used Slack just as a messenger, but than everything changed when emojis came… Since then, we use them for all kinds of purposes. For example we take polls with them to see were we will eat lunch or we capture unforgettable moments by creating new emojis. The possibilities are limitless! But since we use them so much, I was wondering how often do we use them. And when? And which is the top-emoji?! Is it just the thumbsup?

Cornell Statistical Consulting Unit News Archive

     Part 1: Data Cleaning and Exploratory Data Analysis

By Laura Ellis

A long time ago (5 years) I wrote a blog post on tapply. Back then I was just getting into programming and I thought the possibilities of tapply were amazing. So it seems, do many others as it’s become one of my most viewed articles.