Efficiency and productivity indices are measured using this package. This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations.

Computes various geospatial indices of socioeconomic deprivation and disparity in the United States. Some indices are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other indices are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (NDI) are available, including: (1) based on Messer et al. (2006) <doi:10.1007/s11524-006-9094-x> and (2) based on Andrews et al. (2020) <doi:10.1080/17445647.2020.1750066> and Slotman et al. (2022) <doi:10.1016/j.dib.2022.108002> who use variables chosen by Roux and Mair (2010) <doi:10.1111/j.1749-6632.2009.05333.x>. Both are a decomposition of multiple demographic characteristics from the U.S. Census Bureau American Community Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5 (2005-2009 onward), the package can also compute indices of racial or ethnic residential segregation, including but limited to those discussed in Massey & Denton (1988) <doi:10.1093/sf/67.2.281>, and additional indices of socioeconomic disparity.

Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. This package is a parallel implementation of the np package based on the MPI specification that incorporates the Rmpi package (Hao Yu <hyu@stats.uwo.ca>) with minor modifications and we are extremely grateful to Hao Yu for his contributions to the R community. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.

This package provides a Software Development Kit for working with Nixtla''s TimeGPT', a foundation model for time series forecasting. API is an acronym for application programming interface'; this package allows users to interact with TimeGPT via the API'. You can set and validate API keys and generate forecasts via API calls. It is compatible with tsibble and base R. For more details visit <https://www.nixtla.io/docs>.

Predicting the structure of a graph including new nodes and edges using a time series of graphs. Flux balance analysis, a linear and integer programming technique used in biochemistry is used with time series prediction methods to predict the graph structure at a future time point Kandanaarachchi (2025) <doi:10.48550/arXiv.2507.05806>.

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).

This package provides a toolkit for medical records data analysis. The naryn package implements an efficient data structure for storing medical records, and provides a set of functions for data extraction, manipulation and analysis.

Data sets and nonlinear regression models dedicated to predictive microbiology.

This package provides tools for traversing and working with National Hydrography Dataset Plus (NHDPlus) data. All methods implemented in nhdplusTools are available in the NHDPlus documentation available from the US Environmental Protection Agency <https://www.epa.gov/waterdata/basic-information>.

This package provides functions to compute the non-negative garrote estimator as proposed by Breiman (1995) <https://www.jstor.org/stable/1269730> with the penalized initial estimators extension as proposed by Yuan and Lin (2007) <https://www.jstor.org/stable/4623260>.

Empirical statistical analysis, visualization and simulation of diffusion and contagion processes on networks. The package implements algorithms for calculating network diffusion statistics such as transmission rate, hazard rates, exposure models, network threshold levels, infectiousness (contagion), and susceptibility. The package is inspired by work published in Valente, et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995) <ISBN: 9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others (2011) <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among others.

Assist novice developers when preparing a single package or a set of integrated packages to submit to CRAN. Automate the following individual or batch processing: check local source packages; build local .tar.gz source files; install packages from local .tar.gz files; detect conflicts between function names in the environment.

This package implements calculation of probability density function, cumulative distribution function, equicoordinate quantile function and survival function, and random numbers generation for the following multivariate distributions: Lomax (Pareto Type II), generalized Lomax, Mardiaâ s Pareto of Type I, Logistic, Burr, Cook-Johnsonâ s uniform, F and Inverted Beta. See Tapan Nayak (1987) <doi:10.2307/3214068>.

NanoString nCounter data are gene expression assays where there is no need for the use of enzymes or amplification protocols and work with fluorescent barcodes (Geiss et al. (2018) <doi:10.1038/nbt1385>). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. NACHO (NAnoString quality Control dasHbOard) is able to analyse the exported NanoString nCounter data and facilitates the user in performing a quality control. NACHO does this by visualising quality control metrics, expression of control genes, principal components and sample specific size factors in an interactive web application.

This package provides functions to fit linear mixed models based on convolutions of the generalized Laplace (GL) distribution. The GL mixed-effects model includes four special cases with normal random effects and normal errors (NN), normal random effects and Laplace errors (NL), Laplace random effects and normal errors (LN), and Laplace random effects and Laplace errors (LL). The methods are described in Geraci and Farcomeni (2020, Statistical Methods in Medical Research) <doi:10.1177/0962280220903763>.

Runs several heterogeneity-robust difference-in-differences (DID) event-study estimators for non-absorbing (i.e., treatment can switch on and off over time, allowing treatment reversal) binary treatments through their respective packages, harmonizes their output onto a common time axis and tidy data structure, and overlays them in a single ggplot2 panel for visual comparison. Supported estimators include those provided by DIDmultiplegtDYN', PanelMatch', and fect', with an optional naive two-way fixed-effects reference series via fixest'. The underlying methods are respectively described in Clement de Chaisemartin and Xavier D'Haultfoeuille. "Difference-in-Differences Estimators of Intertemporal Treatment Effects." The Review of Economics and Statistics (2026) <doi:10.1162/rest_a_01414>, Kosuke Imai, In Song Kim, and Erik H. Wang. "Matching methods for causal inference with timeâ series crossâ sectional data." American Journal of Political Science 67.3 (2023) <doi:10.1111/ajps.12685>, Licheng Liu, Ye Wang, and Yiqing Xu. "A practical guide to counterfactual estimators for causal inference with timeâ series crossâ sectional data." American Journal of Political Science 68.1 (2024) <doi:10.1111/ajps.12723>, and Laurent R. Bergé, Kyle Butts, and Grant McDermott. "Fast and user-friendly econometrics estimations: The R package fixest'." arXiv preprint (2026) <doi:10.48550/arXiv.2601.21749>. A single nabs_event_study() wrapper runs any supported estimator with a common interface; nabs_event_study_simple() provides a one-line front door for quick exploratory runs; the S3 generic as_nabs_event_study() coerces estimator output into a tidy stable schema; and nabs_event_plot() overlays multiple methods on a single ggplot2 panel, with optional naive two-way fixed effects drawn in a neutral color as a reference.

Nonnegative matrix factorization (NMF) is a technique to factorize a matrix with nonnegative values into the product of two matrices. Covariates are also allowed. Parallel computing is an option to enhance the speed and high-dimensional and large scale (and/or sparse) data are allowed. Relevant papers include: Sevinc V., Kontemeniotis N., Perdikis T. and Tsagris M. (2026). Non-negative matrix factorization using the R package nnmf <doi:10.48550/arXiv.2607.20084>, Wang Y. X. and Zhang Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6): 1336-1353 <doi:10.1109/TKDE.2012.51> and Kim H. and Park H. (2008). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2): 713-730 <doi:10.1137/07069239X>.

This package provides functions for working with (grouped) multivariate normal variance mixture distributions (evaluation of distribution functions and densities, random number generation and parameter estimation), including Student's t distribution for non-integer degrees-of-freedom as well as the grouped t distribution and copula with multiple degrees-of-freedom parameters. See <doi:10.18637/jss.v102.i02> for a high-level description of select functionality.

Various visual and numerical diagnosis methods for the nonlinear mixed effect model, including visual predictive checks, numerical predictive checks, and coverage plots (Karlsson and Holford, 2008, <https://www.page-meeting.org/?abstract=1434>).

An online app and command-line utility to import, filter and visualise sleep data. Can be used with sleep data collected from any type of device (e.g. radar, sleep diary,...) as long as the data contains sleep onset and wake-up times for each sleep session.

NIMBLE is a general purpose modeling framework that facilitates the analysis of Markov Chain Monte Carlo (MCMC) models. However, it constructs a complex and variable data structure, which is difficult to use in a general way. The nimbleExtra package provides a set of functions to insulate users from the complexity and variability of NIMBLE data structures. It also complements NIMBLE and other MCMC analysis packages by providing missing functionality to encourage generic programming. Together these features ease the burden of working with MCMC data and encourage more systematic approaches to MCMC analysis.

Motivated by changing administrative boundaries over time, the nuts package can convert European regional data with NUTS codes between versions (2006, 2010, 2013, 2016 and 2021) and levels (NUTS 1, NUTS 2 and NUTS 3). The package uses spatial interpolation as in Lam (1983) <doi:10.1559/152304083783914958> based on granular (100m x 100m) area, population and land use data provided by the European Commission's Joint Research Center.

This package implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.

This package implements several nonparametric regression approaches for the inclusion of covariate information on the receiver operating characteristic (ROC) framework.