Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic ...

    Hello, An example often used in the ABC literature is the g-and-k distribution (e.g. reference [1] below), which is defined through the inverse of its cumulative distribution function (cdf). It is easy to simulate from such distributions by drawing uniform variables and applying the inverse cdf to them. However, since ...

This week’s R bulletin will cover topics on how to round to the nearest desired number, converting and comparing dates and how to remove last x characters from an element. Hope you like this R weekly bulletin. Enjoy reading! Shortcut Keys 1. Comment/uncomment current line/selection – Ctrl+Shift+C 2. ...

I caught the 0.7.0 release of dplyr on my home CRAN server early Friday morning and immediately set out to install it since I’m eager to finish up my sergeant package and get it on CRAN. “Tidyverse” upgrades aren’t trivial for me as I tinker quite a bit with ...

In our latest “R and big data” article we show how to manage intermediate results in non-trivial Apache Spark workflows using R, sparklyr, dplyr, and replyr. Handle management Many Sparklyr tasks involve creation of intermediate or temporary tables. This can be through dplyr::copy_to() and through dplyr::compute(). These ...

Say we have an intervention that is assigned at a group or cluster level but the outcome is measured at an individual level (e.g. students in different schools, eyes on different individuals). And, say this outcome is binary; that is, something happens, or it doesn’t. (This is important, ...

In my last post, I prepared and visually explored time series data. Now, I will use this data to test the timekit package for time series forecasting with machine learning. Forecasting In time series forecasting, we use models to predict future ti...