S4 classes for univariate and multivariate functional data with utility functions. See <doi:10.18637/jss.v093.i05> for a detailed description of the package functionalities and its interplay with the MFPCA package for multivariate functional principal component analysis <https://CRAN.R-project.org/package=MFPCA>.

The goal of gnonadd is to simplify workflows in the analysis of non-additive effects of sequence variants. This includes variance effects (Ivarsdottir et. al (2017) <doi:10.1038/ng.3928>), correlation effects, interaction effects and dominance effects. The package also includes convenience functions for visualization.

Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.

This package provides tools to access, search, and download global 3D building footprint datasets (3D-GloBFP) generated by Che et al. (2024) <doi:10.5194/essd-16-5357-2024>. The package includes functions to retrieve metadata, filter by bounding box, and download building height tiles.

Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.

Constructing matrices for quick prototyping can be a nuisance, requiring the user to think about how to fill the matrix with values using the matrix() function. The %<-% operator solves that issue by allowing the user to construct matrices using code that shows the actual matrices.

This package provides a collection of utilities referred to Exponential Power distribution, also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4).

Identifies differences between versions of a package. Specifically, the functions help determine if there are breaking changes from one package version to the next. The package also includes a stability assessment, to help you determine the overall stability of a package, or even an entire repository.

This package provides functions to measure Alpha, Beta and Gamma Proximity to Irreplaceability. The methods for Alpha and Beta irreplaceability were first described in: Baisero D., Schuster R. & Plumptre A.J. Redefining and Mapping Global Irreplaceability. Conservation Biology 2021;1-11. <doi:10.1111/cobi.13806>.

This package provides functions and mined database from UniProt focusing on post-translational modifications to do single enrichment analysis (SEA) and protein set enrichment analysis (PSEA). Payman Nickchi, Uladzislau Vadadokhau, Mehdi Mirzaie, Marc Baumann, Amir Ata Saei, Mohieddin Jafari (2025) <doi:10.1002/pmic.202400238>.

Calculates the right-tail probability of quadratic forms of Gaussian variables using the skewness-kurtosis ratio matching method, modified Liu-Tang-Zhang method and Satterthwaite-Welch method. The technical details can be found in Hong Zhang, Judong Shen and Zheyang Wu (2020) <arXiv:2005.00905>.

This package provides tools for the integration and exploration of data tables measured on the same set of observational units. The package includes methods to assess similarities among tables, extract common patterns across variable blocks, and create visual summaries that highlight shared structures in multiblock data.

This package provides a flexible framework for definition and application of time/depth- based rules for sets of parameters for single grains that can be used to create artificial sediment profiles. Such profiles can be used for virtual sample preparation and synthetic, for instance, luminescence measurements.

Extends the functionality of the package Synth as detailed in Abadie, Diamond, and Hainmueller (2011) <doi:10.18637/jss.v042.i13>. Includes generating and plotting placebos, post/pre-MSPE (Mean Squared Prediction Error) significance tests and plots, and calculating average treatment effects for multiple treated units.

When updating major or minor R versions all packages should be re-installed. The utilities in this package assist in getting a user up-and-running again by installing all previously installed R packages. The package uses renv to install; immediately replenishing your renv package cache.

Define distribution families and fit them to interval-censored and interval-truncated data, where the truncation bounds may depend on the individual observation. The defined distributions feature density, probability, sampling and fitting methods as well as efficient implementations of the log-density log f(x) and log-probability log P(x0 <= X <= x1) for use in TensorFlow neural networks via the tensorflow package. Allows training parametric neural networks on interval-censored and interval-truncated data with flexible parameterization. Applications include Claims Development in Non-Life Insurance, e.g. modelling reporting delay distributions from incomplete data, see Bücher, Rosenstock (2022) <doi:10.1007/s13385-022-00314-4>.

This package Copynumber KAryotyping of Tumors infers genomic copy number and subclonal structure of human tumors using integrative Bayesian approaches to identify genome-wide aneuploidy at 5MB resolution in single cells data. It separates tumor cells and tumor subclones from normal cells using high-throughput sc-RNAseq data.

Millefy is a tool for visualizing read coverage of scRNA-seq(single-cell RNA sequencing) datasets in genomic contexts. By dynamically and automatically reorder single cells based on locus-specific pseudo time, Millefy highlights cell-to-cell heterogeneity in read coverage of scRNA-seq data.

This package analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).

This package provides an implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature.

This package provides syntax highlighting of R code, specifically designed for the needs of RMarkdown packages like pkgdown, hugodown, and bookdown. It includes linking of function calls to their documentation on the web, and automatic translation of ANSI escapes in output to the equivalent HTML.

This package provides a generic object traverser for Ruby. It takes the object and recursively yields:

• the given object

• instance variables, class variables, constants

• Hash keys and values

• Enumerable members

• Struct members

• Data members

• Range begins and ends

dStruct identifies differentially reactive regions from RNA structurome profiling data. dStruct is compatible with a broad range of structurome profiling technologies, e.g., SHAPE-MaP, DMS-MaPseq, Structure-Seq, SHAPE-Seq, etc. See Choudhary et al., Genome Biology, 2019 for the underlying method.

EpiTxDb facilitates the storage of epitranscriptomic information. More specifically, it can keep track of modification identity, position, the enzyme for introducing it on the RNA, a specifier which determines the position on the RNA to be modified and the literature references each modification is associated with.