This package is developed for facilitating parallel computing in R. It is capable to create an R object in the shared memory space and share the data across multiple R processes. It avoids the overhead of memory dulplication and data transfer, which make sharing big data object across many clusters possible.

Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data.

This package provides a number of utility functions for handling single-cell RNA-seq data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix.

This package provides an implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages.

This is a package for saving SummarizedExperiments into file artifacts, and loading them back into memory. This is a more portable alternative to serialization of such objects into RDS files. Each artifact is associated with metadata for further interpretation; downstream applications can enrich this metadata with context-specific properties.

This package provides a string allocator for allocating many write-once strings. This library is primarily useful for parsing where you need to repeatedly build many strings, use them, and then throw them away. Instead of allocating many independent strings, this library will put them all in the same buffer.

Simple interpolation methods designed to be used from C code. Supports constant, linear and spline interpolation. An R wrapper is included but this package is primarily designed to be used from C code using LinkingTo'. The spline calculations are classical cubic interpolation, e.g., Forsythe, Malcolm and Moler (1977) <ISBN: 9780131653320>.

There are many estimators of false discovery rate. In this package we compute the Nonlocal False Discovery Rate (NFDR) and the estimators of local false discovery rate: Corrected False discovery Rate (CFDR), Re-ranked False Discovery rate (RFDR) and the blended estimator. Bickel, D.R., Rahal, A. (2019) <https://tinyurl.com/kkdc9rk8>.

This package provides a wrapper for circlize'. All components are based on classes and objects. Users can use the addition symbol (+) to combine components for a circular visualization with ggplot2 style.The package is described in Zhang Z, Cao T, Huang Y and Xia Y (2025) <doi:10.3389/fgene.2025.1535368>.

Free United Kingdom National Health Service (NHS) and other healthcare, or population health-related data for education and training purposes. This package contains synthetic data based on real healthcare datasets, or cuts of open-licenced official data. This package exists to support skills development in the NHS-R community: <https://nhsrcommunity.com/>.

Converts an XLSForm (survey in Excel') into a well-structured Word document, including sections, skip logic, options, and question labels. Designed to support survey documentation, training materials, and data collection workflows. The package was developed based on field experience with XLSForm and humanitarian operations, aiming to streamline documentation and enhance training efficiency.

MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes.

NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis.

This package provides functions for differential chromatin interaction analysis between two single-cell Hi-C data groups. It includes tools for imputation, normalization, and differential analysis of chromatin interactions. The package implements pooling techniques for imputation and offers methods to normalize and test for differential interactions across single-cell Hi-C datasets.

The package contains local copy of the Synaptic proteome database. On top of this it provide a set of utility R functions to query and analyse its content. It allows extraction of information for specific genes and building the protein-protein interaction graph for gene sets, synaptic compartments, and brain regions.

Emacs Org Roam is a solution for taking non-hierarchical notes with Org mode. Notes are captured without hierarchy and are connected by tags. Notes can be found and created quickly. Org Roam should also work as a plug-and-play solution for anyone already using Org mode for their personal wiki.

Emacs Org Roam is a solution for taking non-hierarchical notes with Org mode. Notes are captured without hierarchy and are connected by tags. Notes can be found and created quickly. Org Roam should also work as a plug-and-play solution for anyone already using Org mode for their personal wiki.

Bagging bandwidth selection methods for the Parzen-Rosenblatt and Nadaraya-Watson estimators. These bandwidth selectors can achieve greater statistical precision than their non-bagged counterparts while being computationally fast. See Barreiro-Ures et al. (2020) <doi:10.1093/biomet/asaa092> and Barreiro-Ures et al. (2021) <doi:10.48550/arXiv.2105.04134>.

Given an expression matrix from a bulk sequencing experiment, pre-processes it and creates a shiny app for interactive data analysis and visualisation. The app contains quality checks, differential expression analysis, volcano and cross plots, enrichment analysis and gene regulatory network inference, and can be customised to contain more panels by the user.

Uses non-linear regression to statistically compare two circadian rhythms. Groups are only compared if both are rhythmic (amplitude is non-zero). Performs analyses regarding mesor, phase, and amplitude, reporting on estimates and statistical differences, for each, between groups. Details can be found in Parsons et al (2020) <doi:10.1093/bioinformatics/btz730>.

This package provides an array of statistical models common in causal inference such as standardization, IP weighting, propensity matching, outcome regression, and doubly-robust estimators. Estimates of the average treatment effects from each model are given with the standard error and a 95% Wald confidence interval (Hernan, Robins (2020) <https://miguelhernan.org/whatifbook/>).

Simple feature stores and tools for creating personalised feature stores. diseasystore powers feature stores which can automatically link and aggregate features to a given stratification level. These feature stores are automatically time-versioned (powered by the SCDB package) and allows you to easily and dynamically compute features as part of your continuous integration.

Constructs dynamic optimal shrinkage estimators for the weights of the global minimum variance portfolio which are reconstructed at given reallocation points as derived in Bodnar, Parolya, and Thorsén (2021) (<arXiv:2106.02131>). Two dynamic shrinkage estimators are available in this package. One using overlapping samples while the other use nonoverlapping samples.

An index measuring the amount of information brought by forecasts for extreme events, subject to calibration, is computed. This index is originally designed for weather or climate forecasts, but it may be used in other forecasting contexts. This is the implementation of the index in Taillardat et al. (2019) <arXiv:1905.04022>.