From our blog

New nonlinear least squares solvers in R with {gslnls}

on May 17, 2022

Introduction Solving a nonlinear least squares problem consists of minimizing a least squares objective function made up of residuals $g_1(\boldsymbol{\theta}), \ldots, g_n(\boldsymbol{\theta})$ that are nonlinear functions of the parameters of interest $\boldsymbol{\theta} = (\theta_1,\ldots, \theta_p)'$:$$ \boldsymbol{\theta}^* \ = \ \arg \min_{\boldsymbol{\theta}} \frac{1}{2} \Vert g(\boldsymbol{\theta}) \Vert^2 $$ In the context of regression, this problem is usually formulated as:$$ \begin{align} \boldsymbol{\theta}^* & \ = \ \arg \min_{\boldsymbol{\theta}} \frac{1}{2} \Vert \boldsymbol{y} - f(\boldsymbol{\theta}) \Vert^2 \\ & \ = \ \arg \min_{\boldsymbol{\theta}} \frac{1}{2} \sum_{i = 1}^n (y_i - f_i(\boldsymbol{\theta}))^2 \end{align} $$

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Rotating log files in Rust, and reassembling them for inspection

on February 15, 2022

Context Open Analytics is working on a huge data processing pipeline in the context of high-content imaging. The latest work to this end is a new product that will work in conjunction with Phaedra as part of the pipeline which now covers much more ground than before. The entire backend of this new component is written in Rust and consists of several web servers.Proper log rotation has been a focus as of late, since it is critical to be able to keep on top of things in this massive processing pipeline.

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ShinyProxy 2.6.0

on November 8, 2021

ShinyProxy 2.6.0 Today we release version 2.6.0 of ShinyProxy which includes over forty improvements. ShinyProxy is a novel, open source platform to deploy Shiny apps for the enterprise or larger organizations. Nevertheless, ShinyProxy is also an excellent choice for deploying a handful of apps in smaller organizations. While ShinyProxy is generally used to host Shiny applications, it has always been designed with the idea to host any application that can be packaged into a Docker container.

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Serverless Fan-Out in Vaccine Research

on March 11, 2021

Serverless Fan-Out in Vaccine Research Discovering new vaccines involves intensive data science: the 3D structure and shape of viruses need to be matched to the 3D structure of hundreds of thousands of proteins that can potentially bind on the virus surface. We will not go into the multiple challenges this problem poses from a methodological point of view, but focus on how to deal with the hundreds of thousands of proteins that enter the arena.

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