The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".

This package provides a set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example to export to a PLINK set.

Unofficial client for Sentry <https://sentry.io>, a self-hosted or cloud-based error-monitoring service. It will inform about errors in real-time, and includes integration with the Plumber package.

Data processing and visualizations for rodent vocalizations exported from DeepSqueak'. These functions are compatible with the SqueakR Shiny Dashboard, which can be used to visualize experimental results and analyses.

An R client for the Trello API. Supports free-tier features such as access to private boards, creating and updating cards and other resources, and downloading data in a structured way.

Fast calculation of the Subtree Prune and Regraft (SPR), Tree Bisection and Reconnection (TBR) and Replug distances between unrooted trees, using the algorithms of Whidden and Matsen (2017) <arxiv:1511.07529>.

Forecasting univariate time series with Variational Mode Decomposition (VMD) based time delay neural network models.For method details see Konstantin, D.and Dominique, Z. (2014). <doi:10.1109/TSP.2013.2288675>.

Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at <https://arxiv.org/abs/1604.07451>.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was AG\_probe\_tab.

This package provides panels summarising data points in hexagonal bins for `iSEE`. It is part of `iSEEu`, the iSEE universe of panels that extend the `iSEE` package.

This package helps users to work with TF metadata from various sources. Significant catalogs of TFs and classifications thereof are made available. Tools for working with motif scans are also provided.

Define a relatively light class for managing Xenium data using Bioconductor. Address use of parquet for coordinates, SpatialExperiment for assay and sample data. Address serialization and use of cloud storage.

This package provides a collection of reference expression datasets with curated cell type labels, for use in procedures like automated annotation of single-cell data or deconvolution of bulk RNA-seq.

This package provides a function to infer pathway activity from gene expression. It contains the linear model inferred in the publication "Perturbation-response genes reveal signaling footprints in cancer gene expression".

This package provides functions for regulation, decomposition and analysis of space-time series. The pastecs library is a PNEC-Art4 and IFREMER initiative to bring PASSTEC 2000 functionalities to R.

This package provides a date-time library with high-level primitives that are designed to be difficult to misuse and have reasonable performance. It's heavily inspired by the Temporal project.

This package provides some helpful extensions and modifications to the ggplot2 package to combine multiple ggplot2 plots into one and label them with letters, as is often required for scientific publications.

Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the RMarkdown vignette: "'refitME R-package tutorial".

Robust multivariate methods for high dimensional data including outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).

Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes.

The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided as a data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98), other curves (e.g., NOTCal04 [van der Plicht et al. 2004], Arnold & Libby 1951) and postbomb curves. Also provided are functions to copy the curves into memory, and to read, query and plot the data underlying the IntCal20 curves.

This package provides functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks <doi:10.1093/bioinformatics/btq124>.

This package provides functions for training extreme gradient boosting model using propensity score A-learning and weight-learning methods. For further details, see Liu et al. (2024) <doi:10.1093/bioinformatics/btae592>.

Can be useful for finding associations among different positions in a position-wise aligned sequence dataset. The approach adopted for finding associations among positions is based on the latent multivariate normal distribution.