This package provides a minimal-dependency R client for OpenAI'-compatible text-to-speech APIs (see <https://developers.openai.com/api/reference/resources/audio>). Supports OpenAI', ElevenLabs', local servers ('Chatterbox', LM Studio', OpenWebUI', AnythingLLM'), and the chatterbox package for in-process synthesis. Provides a unified interface for speech synthesis, voice cloning, and voice design with backend-specific parameters.

Converts text into speech using various text-to-speech (TTS) engines and provides an unified interface for accessing their functionality. With this package, users can easily generate audio files of spoken words, phrases, or sentences from plain text data. The package supports multiple TTS engines, including Google's Cloud Text-to-Speech API', Amazon Polly', Microsoft's Cognitive Services Text to Speech REST API', the Speechify Text-to-Speech API', and a free TTS engine called Coqui TTS'.

Implementation of unconditional Bernoulli Scan Statistic developed by Kulldorff et al. (2003) <doi:10.1111/1541-0420.00039> for hierarchical tree structures. Tree-based Scan Statistics are an exploratory method to identify event clusters across the space of a hierarchical tree.

Conditional logistic regression with longitudinal follow up and individual-level random coefficients: A stable and efficient two-step estimation method.

This package provides a set of vectorised functions to calculate medical equations used in transplantation, focused mainly on transplantation of abdominal organs. These functions include donor and recipient risk indices as used by NHS Blood & Transplant, OPTN/UNOS and Eurotransplant, tools for quantifying HLA mismatches, functions for calculating estimated glomerular filtration rate (eGFR), a function to calculate the APRI (AST to platelet ratio) score used in initial screening of suitability to receive a transplant from a hepatitis C seropositive donor and some biochemical unit converter functions. All functions are designed to work with either US or international units. References for the equations are provided in the vignettes and function documentation.

Consolidates and calculates different sets of time-series features from multiple R and Python packages including Rcatch22 Henderson, T. (2021) <doi:10.5281/zenodo.5546815>, feasts O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>, tsfeatures Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020) <https://CRAN.R-project.org/package=tsfeatures>, tsfresh Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, TSFEL Barandas, M., et al. (2020) <doi:10.1016/j.softx.2020.100456>, and Kats Facebook Infrastructure Data Science (2021) <https://facebookresearch.github.io/Kats/>.

The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Paper: Albrecht, Marco, et al. (2017)<DOI:10.1186/s12859-016-1440-8>.

This package provides a tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. TransProR is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include limma (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), edgeR (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), DESeq2 (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.

This package provides a tool to rectangle a nested list, that is to convert it into a tibble'. This is done automatically or according to a given specification. A common use case is for nested lists coming from parsing JSON files, or the JSON responses of REST APIs'. Rectangling uses the vctrs package, and therefore offers a wide support of vector types.

This package provides a wrapper for the TexTra API <https://mt-auto-minhon-mlt.ucri.jgn-x.jp/>, a web service for translating texts between different languages. TexTra API account is required to use the service.

This package contains logic for single sample gene set testing of cancer transcriptomic data with adjustment for normal tissue-specificity. Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.

Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) <doi:10.1016/j.foodqual.2015.06.017>, Castura, Baker, and Ross (2016) <doi:10.1016/j.foodqual.2016.06.011>, and Pineau et al. (2009) <doi:10.1016/j.foodqual.2009.04.005>.

This package performs multiple comparison analyses using Tukey's Honestly Significant Difference (HSD) test, with intuitive letter grouping of means for balanced and unbalanced designs. Accepts input from formula', aov', lm', aovlist', and lmerMod objects, including straightforward handling of interactions. For more details see Tukey (1949) <doi:10.2307/3001913>.

Create structured, formatted HTML tables of in a flexible and convenient way.

Adds some functions to help in your coding etiquette. tinycodet primarily focuses on 4 aspects. 1) Safer decimal (in)equality testing, standard-evaluated alternatives to with() and aes(), and other functions for safer coding. 2) A new package import system, that attempts to combine the benefits of using a package without attaching it, with the benefits of attaching a package. 3) Extending the string manipulation capabilities of the stringi R package. 4) Reducing repetitive code. Besides linking to Rcpp', tinycodet has only one other dependency, namely stringi'.

Computes the product moments of the truncated multivariate normal distribution, particularly for cases involving patterned variance-covariance matrices. It also has the capability to calculate these moments with arbitrary positive-definite matrices, although performance may degrade for high-dimensional variables.

This package implements an algorithm for generating maps, known as tile maps, in which each region is represented by a single tile of the same shape and size. The algorithm was first proposed in "Generating Tile Maps" by Graham McNeill and Scott Hale (2017) <doi:10.1111/cgf.13200>. Functions allow users to generate, plot, and compare square or hexagon tile maps.

This is a collection of functions optimized for working with various kinds of text matrices. Focusing on the text matrix as the primary object - represented either as a base R dense matrix or a Matrix package sparse matrix - allows for a consistent and intuitive interface that stays close to the underlying mathematical foundation of computational text analysis. In particular, the package includes functions for working with word embeddings, text networks, and document-term matrices. Methods developed in Stoltz and Taylor (2019) <doi:10.1007/s42001-019-00048-6>, Taylor and Stoltz (2020) <doi:10.1007/s42001-020-00075-8>, Taylor and Stoltz (2020) <doi:10.15195/v7.a23>, and Stoltz and Taylor (2021) <doi:10.1016/j.poetic.2021.101567>.

Checks LaTeX documents and .bib files for typing errors, such as spelling errors, incorrect quotation marks. Also provides useful functions for parsing and linting bibliography files.

Implementation of ZENIT-POLAR substitution cipher method of encryption using by default the TENIS-POLAR cipher. This last cipher of encryption became famous through the collection of Brazilian books "Os Karas" by the author Pedro Bandeira. For more details, see "A Cryptographic Dictionary" (GC&CS, 1944).

Plot official statistics time series conveniently: automatic legends, highlight windows, stacked bar chars with positive and negative contributions, sum-as-line option, two y-axes with automatic horizontal grids that fit both axes and other popular chart types. tstools comes with a plethora of defaults to let you plot without setting an abundance of parameters first, but gives you the flexibility to tweak the defaults. In addition to charts, tstools provides a super fast, data.table backed time series I/O that allows the user to export / import long format, wide format and transposed wide format data to various file types.

This package provides utility functions for plotting. Includes functions for color manipulation, plot customization, panel size control, data optimization for plots, and layout adjustments.

Reads and parses GEDCOM (Genealogical Data Communication) files, the standard interchange format exported by genealogical software, and converts them into tidy data frames. Individuals, families, life events, and parent-child links are extracted into rectangular structures suitable for pedigree and kinship analysis. Additional helpers summarize file contents, convert place coordinates, and repair malformed records. Wikipedia family tree templates can be parsed into the same tidy structure. For a discussion of these and related data structures see [Hunter et al. (2026) <doi:10.1007/s10519-026-10259-z>].

This package provides an R-interface to the TMDb API (see TMDb API on <https://developers.themoviedb.org/3/getting-started/introduction>). The Movie Database (TMDb) is a popular user editable database for movies and TV shows (see <https://www.themoviedb.org>).