This package provides a reproducible workflow for binning and visualizing NMR (nuclear magnetic resonance) spectra from environmental samples. The nmrrr package is intended for post-processing of NMR data, including importing, merging and, cleaning data from multiple files, visualizing NMR spectra, performing binning/integrations for compound classes, and relative abundance calculations. This package can be easily inserted into existing analysis workflows by users to help with analyzing and interpreting NMR data.

This package provides a robust set of tools for researchers and modelers to construct and define virtual ecological niches using ellipsoid geometries. It enables the identification and extraction of suitable environmental areas, simulation of species occurrence points with various sampling strategies, and visualization of niche boundaries and simulated occurrences in both environmental and geographic space. Inspired by methodologies in NicheA and the virtualspecies R package, nicheR aims to streamline the process of niche conceptualization and data generation for ecological studies. Methodological and theoretical foundations are described in Peterson et al. (2011, ISBN:9780691136882), Etherington et al. (2009) <doi:10.1111/j.1365-2699.2008.02041.x>, Qiao et al. (2015) <doi:10.1111/ecog.01961>, Nunez-Penichet et al. (2021) <doi:10.21425/F5FBG52142>, Cobos and Peterson (2022) <doi:10.17161/bi.v17i.15985>, Alkishe et al. (2022) <doi:10.5194/we-22-33-2022>, and Leroy et al. (2015) <doi:10.1111/ecog.01388>.

This package provides functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186. Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology, 15(6), 447-471.

Ships statistical and mathematical routines from R internal nmath ('Mathlib') as OpenCL C sources under directory inst/cl/', with R wrappers that use the GPU when OpenCL is available at compile time and fall back to stats equivalents otherwise. Aimed at package developers building custom kernels (for example Bayesian GLMs via suggested package glmbayes') using opencltools kernel loaders and related helpers. Contains translated shims, an illustrative GLM-related kernel subsystem, vignettes, and optional GPU acceleration. The ported routines are translated from the nmath ('Mathlib') and Rmath sources of R Core Team (2026) "R: A Language and Environment for Statistical Computing" <doi:10.32614/R.manuals>. OpenCL GPU execution follows the standard described in Stone, Gohara, and Shi (2010) <doi:10.1109/MCSE.2010.69>. The likelihood subgradient simulation methodology implemented by the illustrative GLM kernel subsystem is described in Nygren and Nygren (2006) <doi:10.1198/016214506000000357>.

This package provides functions to access and download data from various NASA APIs <https://api.nasa.gov/#browseAPI>, including: Astronomy Picture of the Day (APOD), Mars Rover Photos, Earth Polychromatic Imaging Camera (EPIC), Near Earth Object Web Service (NeoWs), Earth Observatory Natural Event Tracker (EONET), and NASA Earthdata CMR Search. Most endpoints require a NASA API key for access. Data is retrieved, cleaned for analysis, and returned in a dataframe-friendly format.

This package provides functions for reading cancer record files which follow a format defined by the North American Association of Central Cancer Registries (NAACCR).

Waiting list management using queuing theory to analyse, predict and manage queues, based on the approach described in Fong et al. (2022) <doi:10.1101/2022.08.23.22279117>. Aimed at UK National Health Service (NHS) applications, waiting list summary statistics, target-value calculations, waiting list simulation, and scheduling functions are included.

Package including an interactive Shiny application for testing normality visually.

Catalogue of NBER working papers published between June 1973 and December 2021.

Library to plot performance profiles (Dolan and More (2002) <doi:10.1007/s101070100263>) and nested performance profiles (Hekmati and Mirhajianmoghadam (2019) <doi:10.19139/soic-2310-5070-679>) for a given data frame.

Stanford CoreNLP annotation client. Stanford CoreNLP <https://stanfordnlp.github.io/CoreNLP/index.html> integrates all NLP tools from the Stanford Natural Language Processing Group, including a part-of-speech (POS) tagger, a named entity recognizer (NER), a parser, and a coreference resolution system, and provides model files for the analysis of English. More information can be found in the README.

Tidied data from the ASA 2006 data expo, as well as a number of useful other related data sets.

This package provides an htmlwidgets <https://www.htmlwidgets.org/> interface to NGL.js <http://nglviewer.org/ngl/api/>. NGLvieweR can be used to visualize and interact with protein databank ('PDB') and structural files in R and Shiny applications. It includes a set of API functions to manipulate the viewer after creation in Shiny.

Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at <arXiv:1911.04387>.

Body Shape and related measurements from the US National Health and Nutrition Examination Survey (NHANES, 1999-2004). See <https://www.cdc.gov/nchs/nhanes/index.html> for details.

Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more. NCutYX allows clustering of variables, of samples, and both variables and samples (biclustering), while incorporating the dependencies across multiple types of Omics data. (SJ Teran Hidalgo et al (2017), <doi:10.1186/s12864-017-3990-1>).

Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions modeling oriented towards flow-cytometry data preprocessing applications. Method is detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.

This package performs exhaustive or groupwise (backward elimination) variable selection for binary outcome prediction models using cross-validated Net Benefit as the optimization criterion. It supports predictor costs, restricted cubic splines, interaction terms, permutation importance, and parallel computation. It includes visualizations for model comparison and variable importance. References include Vickers AJ & Elkin EB (2006) <doi:10.1177/0272989X06295361>, Van Calster B et al. (2018) <doi:10.1016/j.eururo.2018.08.038>, Vickers AJ et al. (2019) <doi:10.1186/s41512-019-0064-7>, and Baker SG et al. (2012) <doi:10.1515/1557-4679.1395>.

Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). The package provides implementations of optimisation heuristics (Differential Evolution, Genetic Algorithms, Particle Swarm Optimisation, Simulated Annealing and Threshold Accepting), and other optimisation tools, such as grid search and greedy search. There are also functions for the valuation of financial instruments such as bonds and options, for portfolio selection and functions that help with stochastic simulations.

This package performs network meta-analysis using integrated nested Laplace approximations ('INLA') which is described in Guenhan, Held, and Friede (2018) <doi:10.1002/jrsm.1285>. Includes methods to assess the heterogeneity and inconsistency in the network. Contains more than ten different network meta-analysis dataset. INLA package can be obtained from <https://www.r-inla.org>.

This package provides Scilab n1qn1'. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the Scilab optimization documentation located at <https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>. This version uses manually modified code from f2c to make this a C only binary.

Clustering unilayer and multilayer network data by means of finite mixtures is the main utility of netClust.

Factorize binary matrices into rank-k components using the logistic function in the updating process. See e.g. Tomé et al (2015) <doi:10.1007/s11045-013-0240-9> .

Implementation of models for the controlled introduction of errors in classification datasets. This package contains the noise models described in Saez (2022) <doi:10.3390/math10203736> that allow corrupting class labels, attributes and both simultaneously.