Segment Images in Text Lines and Words

Find text lines in scanned images and segment the lines into words. Includes implementations of the paper 'Novel A* Path Planning Algorithm for Line Segmentation of Handwritten Documents' by Surinta O. et al (2014) available at < https://github.com/smeucci/LineSegm>, an implementation of 'A Statistical approach to line segmentation in handwritten documents' by Arivazhagan M. et al (2007) , and a wrapper for an image segmentation technique to detect words in text lines as described in the paper 'Scale Space Technique for Word Segmentation in Handwritten Documents' by Manmatha R. and Srimal N. (1999) paper at , wrapper for code available at < https://github.com/arthurflor23/text-segmentation>. Provides as well functionality to put cursive text in images upright using the approach defined in the paper 'A new normalization technique for cursive handwritten words' by Vinciarelli A. and Luettin J. (2001) .

Building a Concordance of Terms in a Series of Texts

Compute the frequency distribution of a search term in a series of texts. For example, Arthur Conan Doyle wrote a total of 60 Sherlock Holmes stories, comprised of 54 short stories and 4 longer novels. I wanted to test my own subjective impression that, in many of the stories, Sherlock Holmes' popularity was used as bait to induce the reader to read a story that is essentially not primarily a Sherlock Holmes story. I used the term "Holmes" as a search pattern, since Watson would frequently address him by name, or use his name to describe something that he was doing. My hypothesis is that the frequency distribution of the search pattern "Holmes" is a good proxy for the degree to which a story is or is not truly a Sherlock Holmes story. The results are presented in a manuscript that is available as a vignette and online at < https://barryzee.github.io/Concordance/index.html>.

Compute and Illustrate the Multiple Facets of Functional Diversity

Computing functional traits-based distances between pairs of species for species gathered in assemblages allowing to build several functional spaces. The package allows to compute functional diversity indices assessing the distribution of species (and of their dominance) in a given functional space for each assemblage and the overlap between assemblages in a given functional space, see: Chao et al. (2018) , Maire et al. (2015) , Mouillot et al. (2013) , Mouillot et al. (2014) , Ricotta and Szeidl (2009) . Graphical outputs are included. Visit the 'mFD' website for more information, documentation and examples.

Get Gene Sets for Gene Enrichment Analysis

Gene sets are fundamental for gene enrichment analysis. The package 'geneset' enables querying gene sets from public databases including 'GO' (Gene Ontology Consortium. (2004) ), 'KEGG' (Minoru et al. (2000) ), 'WikiPathway' (Marvin et al. (2020) ), 'MsigDb' (Arthur et al. (2015) ), 'Reactome' (David et al. (2011) ), 'MeSH' (Ish et al. (2014) ), 'DisGeNET' (Janet et al. (2017) ), 'Disease Ontology' (Lynn et al. (2011) ), 'Network of Cancer Genes' (Dimitra et al. (2019) ) and 'COVID-19' (Maxim et al. (2020) ). Gene sets are stored in the list object which provides data frame of 'geneset' and 'geneset_name'. The 'geneset' has two columns of term ID and gene ID. The 'geneset_name' has two columns of terms ID and term description.

R Fortunes

A collection of fortunes from the R community.

Bayesian Non-Homogeneous Markov and Mixture Models for Multiple Time Series

Holsclaw, Greene, Robertson, and Smyth (2017) . Bayesian HMM and NHMM modeling for multiple time series. The emission distribution can be mixtures of Exponential, Gamma, Poisson, or Normal distributions, and zero inflation is possible.