This package provides a unified, programmatic interface for searching, browsing, and retrieving metadata from various international organization data repositories that use the National Data Archive ('NADA') software, such as the World Bank, FAO', and the International Household Survey Network ('IHSN'). Functions allow users to discover available data collections, country codes, and access types, perform complex searches using keyword and spatial/temporal filters, and retrieve detailed study information, including file lists and variable-level data dictionaries. It simplifies access to microdata for researchers and policy analysts globally.

Network is a prevalent form of data structure in many fields. As an object of analysis, many distance or metric measures have been proposed to define the concept of similarity between two networks. We provide a number of distance measures for networks. See Jurman et al (2011) <doi:10.3233/978-1-60750-692-8-227> for an overview on spectral class of inter-graph distance measures.

Subsampling methods for big data under different models and assumptions. Starting with linear regression and leading to Generalised Linear Models, softmax regression, and quantile regression. Specifically, the model-robust subsampling method proposed in Mahendran, A., Thompson, H., and McGree, J. M. (2023) <doi:10.1007/s00362-023-01446-9>, where multiple models can describe the big data, and the subsampling framework for potentially misspecified Generalised Linear Models in Mahendran, A., Thompson, H., and McGree, J. M. (2025) <doi:10.48550/arXiv.2510.05902>.

We connect the multi-class Neyman-Pearson classification (NP) problem to the cost-sensitive learning (CS) problem, and propose two algorithms (NPMC-CX and NPMC-ER) to solve the multi-class NP problem through cost-sensitive learning tools. Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021).

Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.

Set of functions implementing the algorithm described in Fernandez Torvisco et al. (2018) for fitting separable nonlinear regression curves. See Fernandez Torvisco, Rodriguez-Arias Fernandez and Cabello Sanchez (2018) <doi:10.2298/FIL1812233T>.

Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs.

This package contains a sample of the 2005 Grade 8 Mathematics data from the National Assessment of Educational Progress (NAEP). This data set is called the NAEP Primer.

Minimize a differentiable function subject to all the variables being non-negative (i.e. >= 0), using a Conjugate-Gradient algorithm based on a modified Polak-Ribiere-Polyak formula as described in (Li, Can, 2013, <https://www.hindawi.com/journals/jam/2013/986317/abs/>).

Instant access to harmonized National Health and Nutrition Examination Survey (NHANES) data spanning 1999-2023. Retrieve pre-processed datasets from reliable cloud storage with automatic type reconciliation and integrated search tools for variables and datasets. Simplifies NHANES data workflows by handling cycle management and maintaining data consistency across survey waves. Data is sourced from <https://www.cdc.gov/nchs/nhanes/>.

Providing a common set of simplified web scraping tools for working with the NHS Data Dictionary <https://datadictionary.nhs.uk/data_elements_overview.html>. The intended usage is to access the data elements section of the NHS Data Dictionary to access key lookups. The benefits of having it in this package are that the lookups are the live lookups on the website and will not need to be maintained. This package was commissioned by the NHS-R community <https://nhsrcommunity.com/> to provide this consistency of lookups. The OpenSafely lookups have now been added <https://www.opencodelists.org/docs/>.

Framework is devoted to mining numerical association rules through the utilization of nature-inspired algorithms for optimization. Drawing inspiration from the NiaARM Python and the NiaARM Julia packages, this repository introduces the capability to perform numerical association rule mining in the R programming language. Fister Jr., Iglesias, Galvez, Del Ser, Osaba and Fister (2018) <doi:10.1007/978-3-030-03493-1_9>.

Calculate various functions needed for design and monitoring clinical trials with negative binomial endpoint with variable follow-up. This version has a few changes compared to the previous version 1.0.0, including (1) correct a typo in Type 1 censoring, mtbnull=bnull and (2) restructure the code to account for shape parameter equal to zero, i.e. Poisson scenario.

Nonparametric Tests for Main Effects, Simple Effects and Interaction Effect with Censored Data and Two Factorial Influencing Variables.

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: 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 a simple function for easier package loading and auto-installation.

Especially when cross-sectional data are observational, effects of treatment selection bias and confounding are best revealed by using Nonparametric and Unsupervised methods to "Design" the analysis of the given data ...rather than the collection of "designed data". Specifically, the "effect-size distribution" that best quantifies a potentially causal relationship between a numeric y-Outcome variable and either a binary t-Treatment or continuous e-Exposure variable needs to consist of BLOCKS of relatively well-matched experimental units (e.g. patients) that have the most similar X-confounder characteristics. Since our NU Learning approach will form BLOCKS by "clustering" experimental units in confounder X-space, the implicit statistical model for learning is One-Way ANOVA. Within Block measures of effect-size are then either [a] LOCAL Treatment Differences (LTDs) between Within-Cluster y-Outcome Means ("new" minus "control") when treatment choice is Binary or else [b] LOCAL Rank Correlations (LRCs) when the e-Exposure variable is numeric with (hopefully many) more than two levels. An Instrumental Variable (IV) method is also provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute information for effect-size inferences when X-Covariates are assumed to influence Treatment choice or Exposure level but otherwise have no direct effects on y-Outcomes. Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to aid Doctor-Patient (or Researcher-Society) communications about Heterogeneous Outcomes. Obenchain and Young (2013) <doi:10.1080/15598608.2013.772821>; Obenchain, Young and Krstic (2019) <doi:10.1016/j.yrtph.2019.104418>.

Essentials for PK/PD (pharmacokinetics/pharmacodynamics) such as area under the curve, (geometric) coefficient of variation, and other calculations that are not part of base R. This is not a noncompartmental analysis (NCA) package.

Adds brute force and multiple starting values to nls.

Geospatial data for creating maps of New South Wales (NSW), Australia, and some helpers to work with common problems like normalising postcodes. Registers its data with cartographer'.

NEON observational data are provided via the NEON Data Portal <https://www.neonscience.org> and NEON API, and can be downloaded and reformatted by the neonUtilities package. NEON observational data (human-observed measurements, and analyses derived from human-collected samples, such as tree diameters and algal chemistry) are published in a format consisting of one or more tabular data files. This package provides tools for performing common operations on NEON observational data, including checking for duplicates and joining tables.

Commodity pricing models are (systems of) stochastic differential equations that are utilized for the valuation and hedging of commodity contingent claims (i.e. derivative products on the commodity) and other commodity related investments. Commodity pricing models that capture market dynamics are of great importance to commodity market participants in order to exercise sound investment and risk-management strategies. Parameters of commodity pricing models are estimated through maximum likelihood estimation, using available term structure futures data of a commodity. NFCP (n-factor commodity pricing) provides a framework for the modeling, parameter estimation, probabilistic forecasting, option valuation and simulation of commodity prices through state space and Monte Carlo methods, risk-neutral valuation and Kalman filtering. NFCP allows the commodity pricing model to consist of n correlated factors, with both random walk and mean-reverting elements. The n-factor commodity pricing model framework was first presented in the work of Cortazar and Naranjo (2006) <doi:10.1002/fut.20198>. Examples presented in NFCP replicate the two-factor crude oil commodity pricing model presented in the prolific work of Schwartz and Smith (2000) <doi:10.1287/mnsc.46.7.893.12034> with the approximate term structure futures data applied within this study provided in the NFCP package.

This package provides a set of functions to visualize National Football League analysis in ggplot2 plots and gt tables.

This package provides a nonvisual procedure for screening time series for nonstationarity in the context of intensive longitudinal designs, such as ecological momentary assessments. The method combines two diagnostics: one for detecting trends (based on the split R-hat statistic from Bayesian convergence diagnostics) and one for detecting changes in variance (a novel extension inspired by Levene's test). This approach allows researchers to efficiently and reproducibly detect violations of the stationarity assumption, especially when visual inspection of many individual time series is impractical. The procedure is suitable for use in all areas of research where time series analysis is central. For a detailed description of the method and its validation through simulations and empirical application, see Zitzmann, S., Lindner, C., Lohmann, J. F., & Hecht, M. (2024) "A Novel Nonvisual Procedure for Screening for Nonstationarity in Time Series as Obtained from Intensive Longitudinal Designs" <https://www.researchgate.net/publication/384354932_A_Novel_Nonvisual_Procedure_for_Screening_for_Nonstationarity_in_Time_Series_as_Obtained_from_Intensive_Longitudinal_Designs>.