Message translation is often managed with po files and the gettext programme, but sometimes another solution is needed. In contrast to po files, a more flexible approach is used as in the Fluent <https://projectfluent.org/> project with R Markdown snippets. The key-value approach allows easier handling of the translated messages.

Analysis of spatial relationships between cell types in spatial transcriptomics data. Spatial proximity is a critical factor in cell-cell communication. The package calculates nearest neighbor distances between specified cell types and provides visualization tools to explore spatial patterns. Applications include studying cell-cell interactions, immune microenvironment characterization, and spatial organization of tissues.

Reconstructs forest stand basal area and stem density at user-specified reference years using tree-level inventory data. The method estimates missing tree ages, back-calculates diameters using species-specific growth rates, and incorporates decay-class-based decomposition to infer mortality timing for dead trees. Results are returned in a tidy long format suitable for analysis and visualization.

For biparental, three and four-way crosses Identity by Descent (IBD) probabilities can be calculated using Hidden Markov Models and inheritance vectors following Lander and Green (<https://www.jstor.org/stable/29713>) and Huang (<doi:10.1073/pnas.1100465108>). One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

This package provides a dynamic model of the big-picture, whole ecosystem effects of hydrodynamics, temperature, nutrients, and fishing on continental shelf marine food webs. The package is described in: Heath, M.R., Speirs, D.C., Thurlbeck, I. and Wilson, R.J. (2020) <doi:10.1111/2041-210X.13510> StrathE2E2: An R package for modelling the dynamics of marine food webs and fisheries. 8pp.

Transform complex statistical output into straightforward, understandable, and context-aware natural language descriptions using Large Language Models (LLMs), making complex analyses more accessible to individuals with varying statistical expertise. It relies on the ellmer package to interface with LLM providers including OpenAI <https://openai.com/>, Google AI Studio <https://aistudio.google.com/>, and Anthropic <https://www.anthropic.com/> (API keys are required and managed via ellmer').

Phenotypic analysis of field trials using mixed models with and without spatial components. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package asreml for the ASReml software, which can be obtained upon purchase from VSN international (<https://vsni.co.uk/software/asreml-r/>).

This package implements an approximate string matching version of R's native match function. It can calculate various string distances based on edits (Damerau-Levenshtein, Hamming, Levenshtein, optimal string alignment), qgrams (q- gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An implementation of soundex is provided as well. Distances can be computed between character vectors while taking proper care of encoding or between integer vectors representing generic sequences.

Phenotypic analysis of data coming from high throughput phenotyping (HTP) platforms, including different types of outlier detection, spatial analysis, and parameter estimation. The package is being developed within the EPPN2020 project (<https://cordis.europa.eu/project/id/731013>). Some functions have been created to be used in conjunction with the R package asreml for the ASReml software, which can be obtained upon purchase from VSN international (<https://vsni.co.uk/software/asreml-r/>).

Standardized accuracy (staccuracy) is a framework for expressing accuracy scores such that 50% represents a reference level of performance and 100% is a perfect prediction. The staccuracy package provides tools for creating staccuracy functions as well as some recommended staccuracy measures. It also provides functions for some classic performance metrics such as mean absolute error (MAE), root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUCROC), as well as their winsorized versions when applicable.

This package produces publication-quality LaTeX, ASCII, and HTML regression tables. Designed as a drop-in replacement for the stargazer package for lm and glm models, with added native support for fixest', plm', and alpaca objects including automatic fixed-effect and random-effect indicator rows and SE-type detection. Standard errors can be supplied as variance-covariance matrices, numeric vectors, or auto-extracted from supported model objects. Follows the interface of Hlavac (2022) <https://CRAN.R-project.org/package=stargazer>.

Analysis of multi environment data of plant breeding experiments following the analyses described in Malosetti, Ribaut, and van Eeuwijk (2013), <doi:10.3389/fphys.2013.00044>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package asreml for the ASReml software, which can be obtained upon purchase from VSN international (<https://vsni.co.uk/software/asreml-r/>).

Given a list of substance compositions, a list of substances involved in a process, and a list of constraints in addition to mass conservation of elementary constituents, the package contains functions to build the substance composition matrix, to analyze the uniqueness of process stoichiometry, and to calculate stoichiometric coefficients if process stoichiometry is unique. (See Reichert, P. and Schuwirth, N., A generic framework for deriving process stoichiometry in enviromental models, Environmental Modelling and Software 25, 1241-1251, 2010 for more details.).

Acquire hourly meteorological data from stations located all over the world. There is a wealth of data available, with historic weather data accessible from nearly 30,000 stations. The available data is automatically downloaded from a data repository and processed into a tibble for the exact range of years requested. A relative humidity approximation is provided using the August-Roche-Magnus formula, which was adapted from Alduchov and Eskridge (1996) <doi:10.1175%2F1520-0450%281996%29035%3C0601%3AIMFAOS%3E2.0.CO%3B2>.

This package provides a comprehensive statistical toolbox for National Statistics Institutes (INS) in Africa. Provides functions for survey data import ('KoboToolbox', ODK', CSPro', Excel', Stata', SPSS'), data processing and validation, weighted statistical analysis (descriptive statistics, cross-tabulations, regression, Human Development Index (HDI), Multidimensional Poverty Index (MPI) following Alkire and Foster (2011) <doi:10.1093/oep/gpr051>, inequalities), visualization (age pyramids, thematic maps, official charts) and dissemination ('SDMX export, DDI metadata, anonymization, Word/PDF reports). Designed to work in resource-constrained environments, offline and in French.

Infrastructure and functions that can be used for integrating Stan (Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>) code into stand alone R packages which in turn use the CmdStan engine which is often accessed through CmdStanR'. Details given in Stan Development Team (2025) <https://mc-stan.org/cmdstanr/>. Using CmdStanR and pre-written Stan code can make package installation easy. Using staninside offers a way to cache user-compiled Stan models in user-specified directories reducing the need to recompile the same model multiple times.

This package provides a pipeline-friendly toolkit for assembling stop motion animations from sequences of still images. Provides functions to read image directories, restructure frame sequences (duplicate, splice, arrange), apply per-frame pixel transformations (rotate, wiggle, flip, flop, blur, scale, crop, trim, border, background), and export the result as a GIF. All transformation functions accept a frames argument to target any subset of frames, bridging the gap between magick functions that operate on an entire image stack and fine-grained stop motion editing. Image processing is performed via ImageMagick Studio LLC (2024) <https://imagemagick.org>.

This package implements multi-study learning algorithms such as merging, the study-specific ensemble (trained-on-observed-studies ensemble) the study strap, the covariate-matched study strap, covariate-profile similarity weighting, and stacking weights. Embedded within the caret framework, this package allows for a wide range of single-study learners (e.g., neural networks, lasso, random forests). The package offers over 20 default similarity measures and allows for specification of custom similarity measures for covariate-profile similarity weighting and an accept/reject step. This implements methods described in Loewinger, Kishida, Patil, and Parmigiani. (2019) <doi:10.1101/856385>.

Stochastic blockmodeling of one-mode and linked networks as presented in Škulj and Žiberna (2022) <doi:10.1016/j.socnet.2022.02.001>. The optimization is done via CEM (Classification Expectation Maximization) algorithm that can be initialized by random partitions or the results of k-means algorithm. The development of this package is financially supported by the Slovenian Research Agency (<https://www.arrs.si/>) within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

Numerically solve and plot solutions of a parametric ordinary differential equations model of growth, death, and respiration of macroinvertebrate and algae taxa dependent on pre-defined environmental factors. The model (version 1.0) is introduced in Schuwirth, N. and Reichert, P., (2013) <DOI:10.1890/12-0591.1>. This package includes model extensions and the core functions introduced and used in Schuwirth, N. et al. (2016) <DOI:10.1111/1365-2435.12605>, Kattwinkel, M. et al. (2016) <DOI:10.1021/acs.est.5b04068>, Mondy, C. P., and Schuwirth, N. (2017) <DOI:10.1002/eap.1530>, and Paillex, A. et al. (2017) <DOI:10.1111/fwb.12927>.

Runs the applicable tests for a comparison in one call and returns standardised result tables. One-sample, two-group, multi-group, factorial and categorical workflows report parametric, rank-based and robust results side by side with effect sizes, confidence intervals and multiplicity-adjusted p-values. Supervised fits, embeddings, clustering and simulators follow the same result contracts. Methods include those of Welch (1947) <doi:10.1093/biomet/34.1-2.28>, Wilcoxon (1945) <doi:10.2307/3001968>, Mann and Whitney (1947) <doi:10.1214/aoms/1177730491>, Kruskal and Wallis (1952) <doi:10.1080/01621459.1952.10483441>, Friedman (1937) <doi:10.1080/01621459.1937.10503522>, Tukey (1949) <doi:10.2307/3001913>, Dunn (1964) <doi:10.1080/00401706.1964.10490181>, Yuen (1974) <doi:10.1093/biomet/61.1.165>, Brunner and Munzel (2000) <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U>, Algina, Keselman and Penfield (2005) <doi:10.1037/1082-989X.10.3.317>, DeLong, DeLong and Clarke-Pearson (1988) <doi:10.2307/2531595>, Sun and Xu (2014) <doi:10.1109/LSP.2014.2337313>, Pencina, D'Agostino, D'Agostino and Vasan (2008) <doi:10.1002/sim.2929>, and Pencina, D'Agostino and Steyerberg (2011) <doi:10.1002/sim.4085>.

This package provides functions for the collection of 3D points and curves using a stereo camera setup.

This package provides tools which allow regression variables to be placed on similar scales, offering computational benefits as well as easing interpretation of regression output.

StatescopeR is an R wrapper around Statescope, a computational framework designed to discover cell states from cell type-specific gene expression profiles inferred from bulk RNA profiles.