Power and ‘fragile’ p-values by @ellis2013nz

Do ‘fragile’ p values tell us anything? I was interested recently to see this article on p values in the psychology literature float across my social media feed. Paul C Bogdan makes the case that the severity of the replication crisis in science can be judged in part by the proportion of p … Continue reading →

Beyond Nelson-Siegel and splines: A model-agnostic Machine Learning framework for discount curve calibration, interpolation and extrapolation

This paper introduces a general machine learning framework for yield curve modeling, in which classical parametric models such as Nelson-Siegel and Svensson serve as special cases within a broader class of functional regression approaches. By linearizing the bond pricing/swap valuation equation, I … Continue reading →

Rethinking Image Formats

Adding images to a web page used to be straightforward. You’d add the img tag to the HTML, set the src attribute to the appropriate URL and, hopefully, write some informative alt text. (You might also add some CSS, either inline or via a stylesheet.) <img src="plot.png" … Continue reading →

Let It Flow: recreating a FACS plot with ggplot

It’s plot recreation time! In this post, we’ll look at how we can recreate a plot in R. I thought it might be useful to provide the solution but also to detail the process I went through to get there. We have a FACS plot taken from a BD FACS Aria … Continue reading →

New Mentoring Team, Same Open Science Spirit

Read it in: Español. We are excited to introduce the new team of mentors for the rOpenSci Champions Program! This year, we have ten very talented individuals, all from Latin America, who bring a unique combination of experience, enthusiasm, and commitment to open science. Some of them have … Continue reading →

R version of Probabilistic Machine Learning (for longitudinal data) Reserving (work in progress)

Work in progress. Comments welcome. !pip install git+https://github.com/Techtonique/mlreserving.git --verbose %load_ext rpy2.ipython %%R install.packages("reticulate") %%R library(reticulate) library(ggplot2) library(dplyr) # Import Python modules pd <- import("pandas") np <- … Continue reading →

introduction to Bayesian methods for the social sciences (18-22 Aug, Università della Svizzera italiana, Lugano)

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Tidyverse with GitHub Copilot for Healthcare Analytics – Part 1

The role of healthcare analytics The primary objective of healthcare analytics is to seek benefits for health administrators, organizations, and patients, most importantly by enhancing patient experiences and improving health outcomes. The healthcare industry generates vast amounts of data, … Continue reading →

Correlation vs Causation: Understanding the Difference

1 Introduction “Correlation is not causation” – it’s a refrain we hear often, yet the distinction between these concepts is deceptively easy to overlook. Correlation refers to a statistical association: when one variable changes, another tends to change as well. Causation, on … Continue reading →

Repost: The Modern R Stack for Production AI

 Reposted from the original at: https://blog.stephenturner.us/p/r-production-ai...Python isn't the only game in town anymore: R can interact with local and cloud LLM APIs, inspect and modify your local R environment and files, implement RAG, computer vision, NLP, evals, & much … Continue reading →

Containerizing Shiny Apps with {shiny2docker}: A Step-by-Step Guide

You can read the original post in its original format on Rtask website by ThinkR here: Containerizing Shiny Apps with {shiny2docker}: A Step-by-Step Guide Deploying a Shiny application to different servers or sharing it with others can be challenging due to differences in R package versions and … Continue reading →

Understanding Basis Spline (B-spline) By Working Through Cox-deBoor Algorithm

I finally understood B-splines by working through the Cox-deBoor algorithm step-by-step, discovering they’re just weighted combinations of basis functions that make non-linear regression linear. What surprised me is going through Bayesian statistics really helped me understand the engine … Continue reading →

LGBTQ+ Pride Month

      This is the start of Pride month. I have friends, acquaintances, and family members in most if not all LGBTQ+ categories, and probably you do too although you may not know it. I wish they all could live lives of being who they want to be without being socially … Continue reading →

Beyond ARMA-GARCH: leveraging model-agnostic Machine Learning and conformal prediction for nonparametric probabilistic stock forecasting (ML-ARCH)

Introduction Probabilistic (not point forecasting) stock forecasting is notably useful for testing trading strategies or risk capital valuation. Because stock prices exhibit a latent stochastic volatility, this type of forecasting methods relies on classical parametric models like ARMA for the … Continue reading →

Benchmarks vs. Baselines: When ‘Above Average’ Isn’t Good Enough

Why relying on external benchmarks in media analytics can be misleading, and how building your own baselines leads to smarter, more context-aware decision … Continue reading →