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.

Enable programmatic interaction with Notion pages, databases, blocks, comments, and users through the Notion API <https://developers.notion.com/>. Provides both synchronous and asynchronous client interfaces for building workflows and automations that integrate with Notion workspaces. Supports all Notion API endpoints including content creation, data retrieval, and workspace management.

This package provides simple methods for centering and scaling of numeric data. Columns or rows can be ignored when normalizing or be normalized jointly.

Infer system functioning with empirical NETwork COMparisons. These methods are part of a growing paradigm in network science that uses relative comparisons of networks to infer mechanistic classifications and predict systemic interventions. They have been developed and applied in Langendorf and Burgess (2021) <doi:10.1038/s41598-021-99251-7>, Langendorf (2020) <doi:10.1201/9781351190831-6>, and Langendorf and Goldberg (2019) <doi:10.48550/arXiv.1912.12551>.

Box-constrained multiobjective optimization using the elitist non-dominated sorting genetic algorithm - NSGA-II. Fast non-dominated sorting, crowding distance, tournament selection, simulated binary crossover, and polynomial mutation are called in the main program. The methods are described in Deb et al. (2002) <doi:10.1109/4235.996017>.

This package provides a system for writing hierarchical statistical models largely compatible with BUGS and JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. NIMBLE includes default methods for MCMC, Laplace Approximation, deterministic nested approximations, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers NIMBLE provides. NIMBLE extends the BUGS'/'JAGS language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the BUGS'/'JAGS language for writing models, one can use NIMBLE for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.

We fit inverse probability weighting estimator and the augmented inverse probability weighting for non-monotone missing at random data.

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'.

Function and data sets in the book entitled "Nonlinear Time Series Analysis with R Applications" B.Guris (2020). The book will be published in Turkish and the original name of this book will be "R Uygulamali Dogrusal Olmayan Zaman Serileri Analizi". It is possible to perform nonlinearity tests, nonlinear unit root tests, nonlinear cointegration tests and estimate nonlinear error correction models by using the functions written in this package. The Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) type unit root tests can be performed using the functions written. In addition, cointegration tests using the Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) models can be applied. It is possible to estimate nonlinear error correction models. The Granger causality test performed using nonlinear models can also be applied.

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

Renders dynamic network data from networkDynamic objects as movies, interactive animations, or other representations of changing relational structures and attributes.

This package provides functions for downloading, calibrating, and analyzing atmospheric isotope data bundled into the eddy covariance data products of the National Ecological Observatory Network (NEON) <https://www.neonscience.org>. Calibration tools are provided for carbon and water isotope products. Carbon isotope calibration details are found in Fiorella et al. (2021) <doi:10.1029/2020JG005862>, and the readme file at <https://github.com/lanl/NEONiso>. Tools for calibrating water isotope products have been added as of 0.6.0, but have known deficiencies and should be considered experimental and unsupported.

Non-parametric dimensionality reduction function. Reduction with and without feature selection. Plot functions. Automated feature selections. Kosztyan et. al. (2024) <doi:10.1016/j.eswa.2023.121779>.

Uses a modified lifting algorithm on which it builds the nondecimated lifting transform. It has applications in wavelet shrinkage.

This package provides methods to reduce confounding bias from unmeasured confounders in observational studies of vaccine efficacy using negative control outcomes.

This package provides a non-parametric test for multi-observer concordance and differences between concordances in (un)balanced data.

Normative data are often used to estimate the relative position of a raw test score in the population. This package allows for deriving regression-based normative data. It includes functions that enable the fitting of regression models for the mean and residual (or variance) structures, test the model assumptions, derive the normative data in the form of normative tables or automatic scoring sheets, and estimate confidence intervals for the norms. This package accompanies the book Van der Elst, W. (2024). Regression-based normative data for psychological assessment. A hands-on approach using R. Springer Nature.

Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) <ISBN:9780124167438> with book entitled Nature-Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvantages of clustering inconsistency when using the traditional approach. The distance measurements option is also provided in order to increase the quality of clustering results. The Fuzzy Geographically Weighted Clustering with nature inspired optimisation algorithm was firstly developed by Arie Wahyu Wijayanto and Ayu Purwarianti (2014) <doi:10.1109/CITSM.2014.7042178> using Artificial Bee Colony algorithm.

An implementation of the National Information Platforms for Nutrition or NiPN's analytic methods for assessing quality of anthropometric datasets that include measurements of weight, height or length, middle upper arm circumference, sex and age. The focus is on anthropometric status but many of the presented methods could be applied to other variables.

This package provides a compact variation of the usual syntax of function declaration, in order to support tidyverse-style quasiquotation of a function's arguments and body.

The aim of nosoi (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. Methods in Ecology and Evolution 11:1002-1007). It is named after the daimones of plague, sickness and disease that escaped Pandora's jar in the Greek mythology. nosoi is able to take into account the influence of multiple variable on the transmission process (e.g. dual-host systems (such as arboviruses), within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations.

This package provides a collection of dynamic network data sets from various sources and multiple authors represented as networkDynamic'-formatted objects.

This package contains a collection of functions for performing different kinds of calculation that are of interest to someone following a diet plan. Calculators for the Basal Metabolic Rate are based on Mifflin et al. (1990) <doi:10.1093/ajcn/51.2.241> and McArdle, W. D., Katch, F. I., & Katch, V. L. (2010, ISBN:9780812109917).

Nonparametric efficiency measurement and statistical inference via DEA type estimators (see Färe, Grosskopf, and Lovell (1994) <doi:10.1017/CBO9780511551710>, Kneip, Simar, and Wilson (2008) <doi:10.1017/S0266466608080651> and Badunenko and Mozharovskyi (2020) <doi:10.1080/01605682.2019.1599778>) as well as Stochastic Frontier estimators for both cross-sectional data and 1st, 2nd, and 4th generation models for panel data (see Kumbhakar and Lovell (2003) <doi:10.1017/CBO9781139174411>, Badunenko and Kumbhakar (2016) <doi:10.1016/j.ejor.2016.04.049>). The stochastic frontier estimators can handle both half-normal and truncated normal models with conditional mean and heteroskedasticity. The marginal effects of determinants can be obtained.