<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/34041?offset=140 https://bioinformaticsonline.com/blog/view/41496/new-machine-learning-packages-in-r Fri, 27 Mar 2020 12:11:21 -0500 https://bioinformaticsonline.com/blog/view/41496/new-machine-learning-packages-in-r <![CDATA[New Machine Learning Packages in R]]> Machine Learning

autokeras v1.0.1: Implements an interface to AutoKeras, an open source software library for automated machine learning. See README for an example.

MTPS v0.1.9: Implements functions to predict simultaneous multiple outcomes based on revised stacking algorithms as described in Xing et al. (2019). See the vignette to get started.

quanteda.textmodels v0.9.1: Implements methods for scaling models and classifiers based on sparse matrix objects representing textual data. It includes implementations of the Laver et al. (2003) wordscores model, the Perry & Benoit’s (2017) class affinity scaling model, and the Slapin & Proksch (2008) wordfish model. See the vignette to get started.

SeqDetect v1.0.7: Implements the automaton model found in Krleža, Vrdoljak & Brčić (2019) to detect and process sequences. See the vignette for examples and theory.

studyStrap v1.0.0: Implements multi-Study Learning algorithms such as Merging, Study-Specific Ensembling (Trained-on-Observed-Studies Ensemble), the Study Strap, and the Covariate-Matched Study Strap. and offers over 20 similarity measures. See Kishida, et al. (2019) for background and the vignette for how to use the package.

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r Fri, 13 Aug 2021 07:44:05 -0500 https://bioinformaticsonline.com/bookmarks/view/43254/quasr-quantification-and-annotation-of-short-reads-in-r <![CDATA[QuasR: Quantification and annotation of short reads in R]]> The QuasR package (short for Quantify and annotate short reads in R) integrates the functionality of several R packages (such as IRanges (Lawrence et al. 2013) and Rsamtools) and external software (e.g. bowtie, through the Rbowtie package, and HISAT2, through the Rhisat2 package). The package aims to cover the whole analysis workflow of typical high throughput sequencing experiments, starting from the raw sequence reads, over pre-processing and alignment, up to quantification. A single R script can contain all steps of a complete analysis, making it simple to document, reproduce or share the workflow containing all relevant details.

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/QuasR/inst/doc/QuasR.html

]]> Neel https://bioinformaticsonline.com/bookmarks/view/44362/biostats-book Mon, 14 Aug 2023 03:11:39 -0500 https://bioinformaticsonline.com/bookmarks/view/44362/biostats-book <![CDATA[Biostats book !]]> https://practical-stats-med-r.netlify.app/

Address of the bookmark: https://practical-stats-med-r.netlify.app/

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