Time Series Prediction with Integrated Tuning

Time series prediction is a critical task in data analysis, requiring not only the selection of appropriate models, but also suitable data preprocessing and tuning strategies. TSPredIT (Time Series Prediction with Integrated Tuning) is a framework that provides a seamless integration of data preprocessing, decomposition, model training, hyperparameter optimization, and evaluation. Unlike other frameworks, TSPredIT emphasizes the co-optimization of both preprocessing and modeling steps, improving predictive performance. It supports a variety of statistical and machine learning models, filtering techniques, outlier detection, data augmentation, and ensemble strategies. More information is available in Salles et al. .

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.

Read and Write ODS Files

Read ODS (OpenDocument Spreadsheet) into R as data frame. Also support writing data frame into ODS file.

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.