Author: R-Bloggers

(This article was first published on Maxwell B. Joseph, and kindly contributed to R-bloggers) Gaussian process (GP) models are computationally demanding for large datasets. Much work has been done to avoid expensive matrix operations that arise in parameter estimation with larger datasets via sparse and/or reduced rank covariance matrices (Datta et al. 2016 provide a nice review). What follows is an implementation of a spatial Gaussian predictive process Poisson GLM in Stan, following Finley et al. 2009: Improving the performance of predictive process modeling for large datasets, with a comparison to a full rank GP model in terms of execution ... Read More

(This article was first published on Thinking inside the box , and kindly contributed to R-bloggers) The big 1.0.0 is here! Following the unsurpassed lead of the FOAAS project, we have arrived at a milestone: Release 1.0.0 is now on CRAN. The rfoaas package provides an interface for R to the most excellent FOAAS service–which itself provides a modern, scalable and RESTful web service for the frequent need to tell someone to f$#@ off. Release 1.0.0 brings fourteen (!!) new access points: back(), bm(), gfy(), think(), keep(), single_(), look(), looking(), no(), give(), zero(), pulp(), sake(), and anyway(). All with documentation ... Read More

(This article was first published on R – Quintuitive, and kindly contributed to R-bloggers) One approach to trading which has been puzzling me lately, is to sit and wait for opportunities. Sounds simplistic, but it is indeed different than, for instance, the asset allocation strategies. In order to be able to even attempt taking advantage of these opportunities, however, we must be able to identify them. Once the opportunities are identified – we can try to explain (forecast) them using historical data. The first step is to define what an opportunity is. It could be anything, for instance we can ... Read More

(This article was first published on Recipes, scripts and genomics, and kindly contributed to R-bloggers) Annotating sets of genomic intervals with genomic annotations such as chromHMM Genomation is an R package to summarize, annotate and visualize genomic intervals. It contains a collection of tools for visualizing and analyzing genome-wide data sets, i.e. RNA-seq, bisulfite sequencing or chromatin-immunoprecipitation followed by sequencing (ChIP-seq) data. Recently we added new features to genomation The new functionalities are available in the latest version of genomation that can be found on its github website. This demo shows the new annotation functions in genomation. The functions can ... Read More

(This article was first published on DataScience+, and kindly contributed to R-bloggers) Before we build stats/machine learning models, it is a good practice to understand which predictors are significant and have an impact on the response variable. In this post we deal with a particular case when both your response and predictor are categorical variables. By the end of this you’d have gained an understanding of what predictive modelling is and what the significance and purpose of chi-square statistic is. We will go through a hypothetical case study to understand the math behind it. We will actually implement a chi-squared ... Read More

(This article was first published on R – biologyforfun, and kindly contributed to R-bloggers) In 2001 the book by Stephen Hubbell on the neutral theory of biodiversity was a major shift from classical community ecology. Before this book the niche-assembly framework was dominating the study of community dynamics. Very briefly under this framework local species composition is the result of the resource available at a particular site and species presence or absence depends on species niche (as defined in Chase 2003). As a result community assembly was seen as a deterministic process and a specific set of species should emerge ... Read More

(This article was first published on R – TomazTsql, and kindly contributed to R-bloggers) I have seen several time, that execute R code using procedure sp_execute_external_script was not valid due to missing library or library dependencies. Problem is – in general – not solved out of the box. But can be solved using and maintaining a list of installed libraries used by Microsoft R services or by simply create a “pre-code” R code to do a check for you. In both cases, user will end up with additional code, but it might be a good check if you are installing ... Read More

(This article was first published on Revolutions, and kindly contributed to R-bloggers) by Joseph Rickert Data Science is all about getting access to interesting data, and it is really nice when some kind soul not only points out an interesting data set but also makes it easy for you to access it. Below is a list of 17 R packages that appeared on CRAN between May 1st and August 8th that, in one way or another, provide access to publicly available data. bigQueryR: Provides an interface to Google's BigQuery. The vignette shows how to use it. blscrapeR: Provides an API ... Read More