R Fortunes

A collection of fortunes from the R community.

Geolocalização De Endereços Brasileiros (Geocoding Brazilian Addresses)

Método simples e eficiente de geolocalizar dados no Brasil. O pacote é baseado em conjuntos de dados espaciais abertos de endereços brasileiros, utilizando como fonte principal o Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE). O CNEFE é publicado pelo Instituto Brasileiro de Geografia e Estatística (IBGE), órgão oficial de estatísticas e geografia do Brasil. (A simple and efficient method for geolocating data in Brazil. The package is based on open spatial datasets of Brazilian addresses, primarily using the Cadastro Nacional de Endereços para Fins Estatísticos (CNEFE), published by the Instituto Brasileiro de Geografia e Estatística (IBGE), Brazil's official statistics and geography agency.)

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>.

Simplify Access to Data from the Amazon Region

Provides tools for downloading and processing data on the Brazilian Amazon region from a variety of official sources. Covers environmental, agricultural, economic, and social indicators, including deforestation and land use, greenhouse gas emissions, climate, agricultural and livestock production, mining, energy, and foreign trade, from providers such as the Brazilian Institute of Geography and Statistics (IBGE), the National Institute for Space Research (INPE), and MapBiomas. Data are cleaned and standardized for analysis at the municipality, state, and regional levels.

Runs Monte Carlo Markov Chain - With Either 'JAGS', 'nimble' or 'greta' - While Adjusting Burn-in and Thinning Parameters

The function runMCMC_btadjust() returns a mcmc.list object which is the output of a Markov Chain Monte Carlo obtained - from either 'JAGS', 'nimble' or 'greta' - after adjusting burn-in and thinning parameters to meet pre-specified criteria in terms of convergence & effective sample size. Used with 'nimble', runMCMC_btadjust() allows extra calculations (e.g. information criteria for model comparison and goodness-of-fit p-values for model diagnosis).

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.

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.

Using R to Install Stuff on Windows OS (Such As: R, 'Rtools', 'RStudio', 'Git', and More!)

R is great for installing software. Through the 'installr' package you can automate the updating of R (on Windows, using updateR()) and install new software. Software installation is initiated through a GUI (just run installr()), or through functions such as: install.Rtools(), install.pandoc(), install.git(), and many more. The updateR() command performs the following: finding the latest R version, downloading it, running the installer, deleting the installation file, copy and updating old packages to the new R installation.

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.