Allows manual creation of themes and logos to be used in applications created using the shinydashboard package. Removes the need to change the underlying css code by wrapping it into a set of convenient R functions.

An add-on package to DImodels for the fitting of biodiversity and ecosystem function relationship study data with multiple ecosystem function responses and/or time points. This package uses the multivariate and repeated measures Diversity-Interactions (DI) methods developed by Kirwan et al. (2009) <doi:10.1890/08-1684.1>, Finn et al. (2013) <doi:10.1111/1365-2664.12041>, and Dooley et al. (2015) <doi:10.1111/ele.12504>.

The dentomedical package provides a comprehensive suite of tools for medical and dental research. It includes automated descriptive statistics, bivariate analysis with intelligent test selection, logistic regression, and diagnostic accuracy assessment. All functions generate publication-ready tables using flextable', ensuring reproducibility and clarity suitable for manuscripts, reports, and clinical research workflows.

This package provides functions for (1) ranking, selecting, and prioritising genes, proteins, and metabolites from high dimensional biology experiments, (2) multivariate hit calling in high content screens, and (3) combining data from diverse sources.

Calculates the desparsified lasso as originally introduced in van de Geer et al. (2014) <doi:10.1214/14-AOS1221>, and provides inference suitable for high-dimensional time series, based on the long run covariance estimator in Adamek et al. (2020) <doi:10.48550/arXiv.2007.10952>. Also estimates high-dimensional local projections by the desparsified lasso, as described in Adamek et al. (2022) <doi:10.48550/arXiv.2209.03218>.

This package provides a framework for representing discrete numeric series (enumerable sets of numbers) that may be finite or infinite. Series can be traversed, combined using arithmetic operations, tested for membership, and queried for limit points ("sinks"), without explicit enumeration of all elements.

This package produces SPSS- and SAS-like output for linear discriminant function analysis and canonical correlation analysis. The methods are described in Manly & Alberto (2017, ISBN:9781498728966), Rencher (2002, ISBN:0-471-41889-7), and Tabachnik & Fidell (2019, ISBN:9780134790541).

This package provides the dose transition pathways (DTP) to project in advance the doses recommended by a model-based design for subsequent patients (stay, escalate, deescalate or stop early) using all the accumulated toxicity information; See Yap et al (2017) <doi: 10.1158/1078-0432.CCR-17-0582>. DTP can be used as a design and an operational tool and can be displayed as a table or flow diagram. The dtpcrm package also provides the modified continual reassessment method (CRM) and time-to-event CRM (TITE-CRM) with added practical considerations to allow stopping early when there is sufficient evidence that the lowest dose is too toxic and/or there is a sufficient number of patients dosed at the maximum tolerated dose.

Adopts the general least squares-based data-driven normalization strategy developed by Heckmann et al. (2011) <doi:10.1186/1471-2105-12-250> to correct for technical variance in gene expression data generated via digital polymerase chain reaction (dPCR). Performs normalization of raw copy numbers and also calculates relative variability metrics that can be used to assess the impact of normalization on variance.

Computational tools for meta-analysis of diagnostic accuracy test. Bootstrap-based computational methods of the confidence interval for AUC of summary ROC curve and some related AUC-based inference methods are available (Noma et al. (2021) <doi:10.1080/23737484.2021.1894408>).

The data consist of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides tools to create or manage such data and functional methods (principal component analysis, multidimensional scaling, cluster analysis, discriminant analysis...) for such probability densities.

This package provides a grammar of data manipulation with data.table', providing a consistent a series of utility functions that help you solve the most common data manipulation challenges.

Datasets and functions to accompany the book Analisis de datos con el programa estadistico R: una introduccion aplicada by Salas-Eljatib (2021, ISBN: 9789566086109). The package helps carry out data management, exploratory analyses, and model fitting.

Implementing algorithms and fitting models when sites (possibly remote) share computation summaries rather than actual data over HTTP with a master R process (using opencpu', for example). A stratified Cox model and a singular value decomposition are provided. The former makes direct use of code from the R survival package. (That is, the underlying Cox model code is derived from that in the R survival package.) Sites may provide data via several means: CSV files, Redcap API, etc. An extensible design allows for new methods to be added in the future and includes facilities for local prototyping and testing. Web applications are provided (via shiny') for the implemented methods to help in designing and deploying the computations.

Data cloud geometry (DCG) applies random walks in finding community structures for social networks. Fushing, VanderWaal, McCowan, & Koehl (2013) (<doi:10.1371/journal.pone.0056259>).

Decodes meshes and point cloud data encoded by the Draco mesh compression library from Google. Note that this is only designed for basic decoding and not intended as a full scale wrapping of the Draco library.

This package provides functions are provided that facilitate the import and analysis of SNP (single nucleotide polymorphism) and silicodart (presence/absence) data. The main focus is on data generated by DarT (Diversity Arrays Technology), however, data from other sequencing platforms can be used once SNP or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived genlight format (package adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of SNP and silicodart data, for reporting on and filtering on various criteria (e.g. CallRate', heterozygosity, reproducibility, maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. dartR supports also the analysis of 3rd party software package such as newhybrid', structure', NeEstimator and blast'. Since version 2.0.3 we also implemented simulation functions, that allow to forward simulate SNP dynamics under different population and evolutionary dynamics. Comprehensive tutorials and support can be found at our github repository: github.com/green-striped-gecko/dartR/. If you want to cite dartR', you find the information by typing citation('dartR') in the console.

Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also called F- designation) is implemented. Dedicated functions ease plotting the results. See Tvedebrink et al. (2012) <doi:10.1016/j.fsigen.2011.08.001>. Compute the distribution of the numbers of alleles in DNA mixtures. See Tvedebrink (2013) <doi:10.1016/j.fsigss.2013.10.142>.

This package provides friendly wrappers for creating duckdb'-backed connections to tabular datasets ('csv', parquet, etc) on local or remote file systems. This mimics the behaviour of "open_dataset" in the arrow package, but in addition to S3 file system also generalizes to any list of http URLs.

This package provides landscape genomic functions to analyse SNP (single nuclear polymorphism) data, such as least cost path analysis and isolation by distance. Therefore each sample needs to have coordinate data attached (lat/lon) to be able to run most of the functions. dartR.spatial is a package that belongs to the dartRverse suit of packages and depends on dartR.base and dartR.data'.

This package provides a function toolkit to facilitate reproducible RNA-Seq Differential Gene Expression (DGE) analysis (Law (2015) <doi:10.12688/f1000research.9005.3>). The tools include both analysis work-flow and utility functions: mapping/unit conversion, count normalization, accounting for unknown covariates, and more. This is a complement/cohort to the DGEobj package that provides a flexible container to manage and annotate Differential Gene Expression analysis results.

Inference by sequential Monte Carlo for dynamic tree regression and classification models with hooks provided for sequential design and optimization, fully online learning with drift, variable selection, and sensitivity analysis of inputs. Illustrative examples from the original dynamic trees paper (Gramacy, Taddy & Polson (2011); <doi:10.1198/jasa.2011.ap09769>) are facilitated by demos in the package; see demo(package="dynaTree").

All datasets and functions required for the examples and exercises of the book "Data Science for Psychologists" (by Hansjoerg Neth, Konstanz University, 2026, <doi:10.5281/zenodo.7229812>), freely available at <https://hneth-ds4psy.share.connect.posit.cloud/>. The book and corresponding courses introduce principles and methods of data science to students of psychology and other biological or social sciences. The ds4psy package primarily provides datasets, but also functions for data generation and manipulation (e.g., of text and time data) and graphics that are used in the book and its exercises. All functions included in ds4psy are designed to be explicit and instructive, rather than efficient or elegant.

Testing and documenting code that communicates with remote databases can be painful. Although the interaction with R is usually relatively simple (e.g. data(frames) passed to and from a database), because they rely on a separate service and the data there, testing them can be difficult to set up, unsustainable in a continuous integration environment, or impossible without replicating an entire production cluster. This package addresses that by allowing you to make recordings from your database interactions and then play them back while testing (or in other contexts) all without needing to spin up or have access to the database your code would typically connect to.