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ShinyProxy 3.0.0

on March 2, 2023

ShinyProxy 3.0.0 Today we release version 3.0.0 of ShinyProxy, providing improvements on all aspects of using and deploying ShinyProxy. 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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Spreadsheets and robust backends: a love story?

on February 12, 2023

The source of every data science project is a dataset or even multiple. In general, scientists prefer to share data using a spreadsheet. This allows to quickly explore, enter and modify data. Software developers on the other hand, prefer to build around properly designed schemes and backends that ensure data integrity. Whoever wins this battle decides where the data is stored: in a type of spreadsheet file or in some kind of database.

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Complete monitoring stack for ShinyProxy on Kubernetes

on September 22, 2022

Introduction For some time now, ShinyProxy is able to export metrics using Prometheus. After releasing this feature, we provided a dashboard for Grafana to visualize these metrics. Although this provides a good basis to build a monitoring setup around ShinyProxy, it does not provide insights in logs or resource usage of individual apps. To fill this gap, we are publishing a GitHub repository containing a comprehensive monitoring set up for ShinyProxy on Kubernetes.

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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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