This package provides a dataframe validation framework for package builders who use dataframes as function parameters. It performs checks on column names, coerces data-types, and checks grouping to make sure user inputs conform to a specification provided by the package author. It provides a mechanism for package authors to automatically document supported dataframe inputs and selectively dispatch to functions depending on the format of a dataframe much like S3 does for classes. It also contains some developer tools to make working with and documenting dataframe specifications easier. It helps package developers to improve their documentation and simplifies parameter validation where dataframes are used as function parameters.

Neighbourhood functions are key components of local-search algorithms such as Simulated Annealing or Threshold Accepting. These functions take a solution and return a slightly-modified copy of it, i.e. a neighbour. The package provides a function neighbourfun() that constructs such neighbourhood functions, based on parameters such as admissible ranges for elements in a solution. Supported are numeric and logical solutions. The algorithms were originally created for portfolio-optimisation applications, but can be used for other models as well. Several recipes for neighbour computations are taken from "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658).

Imports Coral Point Count with Excel extensions (CPCe) point-count annotations and related exported tables, standardizes labels with a user-supplied crosswalk, creates ecological cover summaries, writes quality-control overlays, and exports machine-learning-ready point labels, image patches, sparse masks, and train/validation/test splits. The package is fully local and does not depend on third-party web platforms, user accounts, or other closed services. CPCe methods are described by Kohler and Gill (2006) "Coral Point Count with Excel extensions (CPCe): A Visual Basic program for the determination of coral and substrate coverage using random point count methodology" <doi:10.1016/j.cageo.2005.11.009>.

This package implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions <doi:10.48550/arXiv.1902.00101>. Moreira and Carvalho (2023) analyze power in preprocessing methodologies for datasets with missing values <doi:10.1080/03610918.2023.2234683>.

This package provides a client for the EWAS Atlas web services (<https://ngdc.cncb.ac.cn/ewas/>; Li et al. (2019) <doi:10.1093/nar/gky1027>), allowing users to query epigenome-wide association study (EWAS) data by CpG (cytosine-phosphate-guanine) probe identifier, gene symbol, or genomic region, and to run trait, Gene Ontology, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, and genomic location enrichment analyses on a set of CpG probes. Query functions support concurrent, rate-limited requests to the remote service. Results are returned as tidy data frames with dedicated summary and plotting methods, including word clouds of enriched EWAS Atlas trait terms.

The goal of image2data is to extract images and return them into a data set, especially for teaching data manipulation and data visualization. Basically, the eponymous function takes an image file ('png', tiff', jpeg', bmp') and turn it into a data set, pixels being rows (subjects) and columns (variables) being their coordinate positions (x- and y-axis) and their respective color (in hex codes). The function can return a complete image or a range of color (i.e., contour, silhouette). The data can then be manipulated as would any data set by either creating other related variables (to hide the image) or as a genuine toy data set.

This package provides functions to design and simulate optimal two-stage randomized controlled trials (RCTs) with ordered categorical outcomes, using rank-based tests and interim decision rules for futility and superiority, following Park (2025) <doi:10.1371/journal.pone.0318211>. The functions rule()', op() and design_table() provide a single entry point for constructing designs, evaluating their operating characteristics, and tabulating several designs at once. The separate functions provided for each combination of test statistic and stopping rule up to version 1.0.2 were deprecated in 1.0.3 and are removed here. Please see the package reference manual and the vignette for details.

This package provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) <doi:10.1007/978-3-030-67664-3_13>, MCMC reconciliation of count time series (Corani et al., 2024) <doi:10.1016/j.ijforecast.2023.04.003>, Bottom-Up Importance Sampling (Zambon et al., 2024) <doi:10.1007/s11222-023-10343-y>, methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024) <https://proceedings.mlr.press/v244/zambon24a.html>, analytical reconciliation with Bayesian treatment of the covariance matrix (Carrara et al., 2025) <doi: 10.48550/arXiv.2506.19554>.

This package provides a pipeline for the comparative analysis of collective movement data (e.g. fish schools, bird flocks, baboon troops) by processing 2-dimensional positional data (x,y,t) from GPS trackers or computer vision tracking systems, discretizing events of collective motion, calculating a set of established metrics that characterize each event, and placing the events in a multi-dimensional swarm space constructed from these metrics. The swarm space concept, the metrics and data sets included are described in: Papadopoulou Marina, Furtbauer Ines, O'Bryan Lisa R., Garnier Simon, Georgopoulou Dimitra G., Bracken Anna M., Christensen Charlotte and King Andrew J. (2023) <doi:10.1098/rstb.2022.0068>.

The main aim of this package is to combine the advantage of wavelet and support vector machine models for time series forecasting. This package also gives the accuracy measurements in terms of RMSE and MAPE. This package fits the hybrid Wavelet SVR model for time series forecasting The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Raimundo and Okamoto (2018) <DOI: 10.1109/INFOCT.2018.8356851>.

High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery.

Allows access to data in running instance of Microsoft Excel (e. g. xl[a1] = xl[b2]*3 and so on). Graphics can be transferred with xl[a1] = current.graphics()'. Additionally there are function for reading/writing Excel files - xl.read.file'/'xl.save.file'. They are not fast but able to read/write *.xlsb'-files and password-protected files. There is an Excel workbook with examples of calling R from Excel in the doc folder. It tries to keep things as simple as possible - there are no needs in any additional installations besides R, only VBA code in the Excel workbook. Microsoft Excel is required for this package.

Testing for parallel trends is crucial in the Difference-in-Differences framework. To this end, this package performs equivalence testing in the context of Difference-in-Differences estimation. It allows users to test if pre-treatment trends in the treated group are â equivalentâ to those in the control group. Here, â equivalenceâ means that rejection of the null hypothesis implies that a function of the pre-treatment placebo effects (maximum absolute, average or root mean squared value) does not exceed a pre-specified threshold below which trend differences are considered negligible. The package is based on the theory developed in Dette & Schumann (2024) <doi:10.1080/07350015.2024.2308121>.

Identifies gendered words and phrases in text using a built in dictionary of more than two hundred gendered terms paired with gender neutral alternatives. Reports the share of gendered language in a text, lists every gendered term found together with its suggested neutral replacement, and can rewrite a text in gender neutral form. Plain text files are read with base R, while other document formats such as PDF and Word are supported through the optional readtext package. The dictionary is informed by published guidance on gender inclusive language, including the United Nations guidelines <https://www.un.org/en/gender-inclusive-language/> and the European Parliament guidance on gender neutral language.

This package implements an empirical approach referred to as PeakTrace which uses multiple hydrographs to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path. The method is based on the identification of associated events and uses (linear) regression models to describe translation and retention processes between neighboring hydrographs. Several regression model results are combined to arrive at a power plant-specific model. The approach is proposed and validated in Greimel et al. (2022) <doi:10.1002/rra.3978>. The identification of associated events is based on the event detection implemented in hydropeak'.

Use standard genomics file format (BED) and a table of orthologs to illustrate synteny conservation at the genome-wide scale. Significantly conserved linkage groups are identified as described in Simakov et al. (2020) <doi:10.1038/s41559-020-1156-z> and displayed on an Oxford Grid (Edwards (1991) <doi:10.1111/j.1469-1809.1991.tb00394.x>) or a chord diagram as in Simakov et al. (2022) <doi:10.1126/sciadv.abi5884>. The package provides a function that uses a network-based greedy algorithm to find communities (Clauset et al. (2004) <doi:10.1103/PhysRevE.70.066111>) and so automatically order the chromosomes on the plot to improve interpretability.

Allows production of Microsoft corporate documents from R Markdown by reusing formatting defined in Microsoft Word documents. You can reuse table styles, list styles but also add column sections, landscape oriented pages. Table and image captions as well as cross-references are transformed into Microsoft Word fields, allowing documents edition and merging without issue with references; the syntax conforms to the bookdown cross-reference definition. Objects generated by the officer package are also supported in the knitr chunks. Microsoft PowerPoint presentations also benefit from this as well as the ability to produce editable vector graphics in PowerPoint and also to define placeholder where content is to be added.

Model-implied simulation-based power estimation (MSPE) for nonlinear (and linear) SEM, path analysis and regression analysis. A theoretical framework is used to approximate the relation between power and sample size for given type I error rates and effect sizes. The package offers an adaptive search algorithm to find the optimal N for given effect sizes and type I error rates. Plots can be used to visualize the power relation to N for different parameters of interest (POI). Theoretical justifications are given in Irmer et al. (2024a) <doi:10.31219/osf.io/pe5bj> and detailed description are given in Irmer et al. (2024b) <doi:10.3758/s13428-024-02476-3>.

This package provides a toolset for functional enrichment analysis and visualization, gene/protein/SNP identifier conversion and mapping orthologous genes across species via g:Profiler. The main tools are:

1. g:GOSt, functional enrichment analysis and visualization of gene lists;

2. g:Convert, gene/protein/transcript identifier conversion across various namespaces;

3. g:Orth, orthology search across species;

4. g:SNPense, mapping SNP rs identifiers to chromosome positions, genes and variant effects.

This package is an R interface corresponding to the 2019 update of g:Profiler and provides access to versions e94_eg41_p11 and higher.

An implementation of the generalized consensus estimators used to assign the reference value in a key comparison exercise. This can also be applied to any interlaboratory study. Given a set of different sources, primary laboratories or measurement methods this package provides an evaluation of the variance components according to the selected statistical method for consensus building. It also implements the comparison among different consensus builders and evaluates the participating method or sources against the consensus reference value. Based on a diverse set of references, DerSimonian-Laird (1986) <doi:10.1016/0197-2456(86)90046-2>, for a complete list of references look at the reference section in the package documentation.

This package provides lightweight R wrappers for querying and listing models from several hosted foundation model inference platforms, currently including OpenAI <https://developers.openai.com/api/docs/>, Google Gemini <https://ai.google.dev/gemini-api/docs>, Groq <https://console.groq.com/docs/>, OpenRouter <https://openrouter.ai/docs/>, Cerebras <https://inference-docs.cerebras.ai/>, and Ollama Cloud <https://docs.ollama.com/cloud>. The package is designed for simple inference workflows and quick experimentation, with minimal abstraction and a consistent interface across providers. It includes helper functions for model discovery, text generation, embeddings, image generation, and multimodal inputs, while leaving room for future support of provider-specific parameters and advanced options.

Survival analysis using a flexible Bayesian model for individual-level right-censored data, optionally combined with aggregate data on counts of survivors in different periods of time. An M-spline is used to describe the hazard function, with a prior on the coefficients that controls over-fitting. Proportional hazards or flexible non-proportional hazards models can be used to relate survival to predictors. Additive hazards (relative survival) models, waning treatment effects, and mixture cure models are also supported. Priors can be customised and calibrated to substantive beliefs. Posterior distributions are estimated using Stan', and outputs are arranged in a tidy format. See Jackson (2023) <doi:10.1186/s12874-023-02094-1>.

Core Hunter is a tool to sample diverse, representative subsets from large germplasm collections, with minimum redundancy. Such so-called core collections have applications in plant breeding and genetic resource management in general. Core Hunter can construct cores based on genetic marker data, phenotypic traits or precomputed distance matrices, optimizing one of many provided evaluation measures depending on the precise purpose of the core (e.g. high diversity, representativeness, or allelic richness). In addition, multiple measures can be simultaneously optimized as part of a weighted index to bring the different perspectives closer together. The Core Hunter library is implemented in Java 8 as an open source project (see <http://www.corehunter.org>).

This package provides tools for generating hierarchical exploded-view maps from dense administrative boundary data. The package applies rigid-body translations to polygon geometries using a centroid-driven vector field, preserving the internal geometry of each feature while separating units within and across regions. Parameters can be derived analytically from dataset geometry using closed-form models for regional separation and local expansion. The package also includes grouped layouts, optional bounded collision refinement, and an interactive focus-map widget for selected-area inspection in htmlwidgets and Shiny'. It implements the methodology described in George Arthur (2026) <https://github.com/PrigasG/explodemap> "A Hierarchical Vector-Based Framework for Multi-Scale Exploded-View Cartography".