Accompanies the book "Nonparametric Statistical Methods Using R, 2nd Edition" by Kloke and McKean (2024, ISBN:9780367651350). Includes methods, datasets, and random number generation useful for the study of robust and/or nonparametric statistics. Emphasizes classical nonparametric methods for a variety of designs --- especially one-sample and two-sample problems. Includes methods for general scores, including estimation and testing for the two-sample location problem as well as Hogg's adaptive method.

This package contains the functions for testing the spatial patterns (of segregation, spatial symmetry, association, disease clustering, species correspondence, and reflexivity) based on nearest neighbor relations, especially using contingency tables such as nearest neighbor contingency tables (Ceyhan (2010) <doi:10.1007/s10651-008-0104-x> and Ceyhan (2017) <doi:10.1016/j.jkss.2016.10.002> and references therein), nearest neighbor symmetry contingency tables (Ceyhan (2014) <doi:10.1155/2014/698296>), species correspondence contingency tables and reflexivity contingency tables (Ceyhan (2018) <doi:10.2436/20.8080.02.72> for two (or higher) dimensional data. The package also contains functions for generating patterns of segregation, association, uniformity in a multi-class setting (Ceyhan (2014) <doi:10.1007/s00477-013-0824-9>), and various non-random labeling patterns for disease clustering in two dimensional cases (Ceyhan (2014) <doi:10.1002/sim.6053>), and for visualization of all these patterns for the two dimensional data. The tests are usually (asymptotic) normal z-tests or chi-square tests.

An API client for NASA POWER global meteorology, surface solar energy and climatology data API. POWER (Prediction Of Worldwide Energy Resources) data are freely available for download with varying spatial resolutions dependent on the original data and with several temporal resolutions depending on the POWER parameter and community. This work is funded through the NASA Earth Science Directorate Applied Science Program. For more on the data themselves, the methodologies used in creating, a web-based data viewer and web access, please see <https://power.larc.nasa.gov/>.

Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003) <doi:10.1093/aob/mcg029>. There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Twenty eight (28) new self-start (SS) functions in total. This package also supports the publication Nonlinear regression Models and applications in agricultural research by Archontoulis and Miguez (2015) <doi:10.2134/agronj2012.0506>, a book chapter with similar material <doi:10.2134/appliedstatistics.2016.0003.c15> and a publication by Oddi et. al. (2019) in Ecology and Evolution <doi:10.1002/ece3.5543>. The function nlsLMList uses nlsLM for fitting, but it is otherwise almost identical to nlme::nlsList'.In addition, this release of the package provides functions for conducting simulations for nlme and gnls objects as well as bootstrapping. These functions are intended to work with the modeling framework of the nlme package. It also provides four vignettes with extended examples.

An R-package for calculating sample size of a survival trial with or without cure fractions.

This package implements some risk measures for (financial) networks, such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity.

Segregation is a network-level property such that edges between predefined groups of vertices are relatively less likely. Network homophily is a individual-level tendency to form relations with people who are similar on some attribute (e.g. gender, music taste, social status, etc.). In general homophily leads to segregation, but segregation might arise without homophily. This package implements descriptive indices measuring homophily/segregation. It is a computational companion to Bojanowski & Corten (2014) <doi:10.1016/j.socnet.2014.04.001>.

This package provides a finite-population significance test of the sharp causal null hypothesis that treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers. A generalized likelihood ratio test statistic is used, and the resulting p-value is exact. Currently, it is assumed that there are only Compliers and Never Takers in the population.

This package provides a network-guided penalized regression framework that integrates network characteristics from Gaussian graphical models with partial penalization, accounting for both network structure (hubs and non-hubs) and clinical covariates in high-dimensional omics data, including transcriptomics and proteomics. The full methodological details can be found in our recent preprint by Ahn S and Oh EJ (2025) <doi:10.48550/arXiv.2505.22986>.

Posterior sampling in several commonly used distributions using normalized power prior as described in Duan, Ye and Smith (2006) <doi:10.1002/env.752> and Ibrahim et.al. (2015) <doi:10.1002/sim.6728>. Sampling of the power parameter is achieved via either independence Metropolis-Hastings or random walk Metropolis-Hastings based on transformation.

The National Ecological Observatory Network (NEON) provides access to its numerous data products through its REST API, <https://data.neonscience.org/data-api/>. This package provides a high-level user interface for downloading and storing NEON data products. Unlike neonUtilities', this package will avoid repeated downloading, provides persistent storage, and improves performance. neonstore can also construct a local duckdb database of stacked tables, making it possible to work with tables that are far to big to fit into memory.

Extends package nat (NeuroAnatomy Toolbox) by providing objects and functions for handling template brains.

This package implements statistical tools for analyzing, simulating, and computing properties of the New Topp-Leone Kumaraswamy Inverse Exponential (NTLKwIEx) distribution. See Atchadé M, Otodji T, and Djibril A (2024) <doi:10.1063/5.0179458> and Atchadé M, Otodji T, Djibril A, and N'bouké M (2023) <doi:10.1515/phys-2023-0151> for details.

Closed testing has been proved powerful for true discovery guarantee. The computation of closed testing is, however, quite burdensome. A general way to reduce computational complexity is to combine partial closed testings for some prespecified feature sets of interest. Partial closed testings are performed at Bonferroni-corrected alpha level to guarantee the lower bounds for the number of true discoveries in prespecified sets are simultaneously valid. For any post hoc chosen sets of interest, coherence property is used to get the lower bound. In this package, we implement closed testing with globaltest to calculate the lower bound for number of true discoveries, see Ningning Xu et.al (2021) <arXiv:2001.01541> for detailed description.

This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010)<doi:10.1145/1835804.1835933>. Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process.

This package implements the navigated weighting (NAWT) proposed by Katsumata (2020) <arXiv:2005.10998>, which improves the inverse probability weighting by utilizing estimating equations suitable for a specific pre-specified parameter of interest (e.g., the average treatment effects or the average treatment effects on the treated) in propensity score estimation. It includes the covariate balancing propensity score proposed by Imai and Ratkovic (2014) <doi:10.1111/rssb.12027>, which uses covariate balancing conditions in propensity score estimation. The point estimate of the parameter of interest as well as coefficients for propensity score estimation and their uncertainty are produced using the M-estimation. The same functions can be used to estimate average outcomes in missing outcome cases.

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>.

Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented.

This package contains functions useful for debugging, set operations on vectors, and UTC date and time functionality. It adds a few vector manipulation verbs to purrr and dplyr packages. It can also generate an R file to install and update packages to simplify deployment into production. The functions were developed at the data science firm Numeract LLC and are used in several packages and projects.

An R interface to the Julia package NeuralEstimators.jl'. The package facilitates the user-friendly development of neural Bayes estimators, which are neural networks that map data to a point summary of the posterior distribution (Sainsbury-Dale et al., 2024, <doi:10.1080/00031305.2023.2249522>). These estimators are likelihood-free and amortised, in the sense that, once the neural networks are trained on simulated data, inference from observed data can be made in a fraction of the time required by conventional approaches. The package also supports amortised Bayesian or frequentist inference using neural networks that approximate the posterior or likelihood-to-evidence ratio (Zammit-Mangion et al., 2025, Sec. 3.2, 5.2, <doi:10.48550/arXiv.2404.12484>). The package accommodates any model for which simulation is feasible by allowing users to define models implicitly through simulated data.

Access the United States National Provider Identifier Registry API <https://npiregistry.cms.hhs.gov/api/>. Obtain and transform administrative data linked to a specific individual or organizational healthcare provider, or perform advanced searches based on provider name, location, type of service, credentials, and other attributes exposed by the API.

Simplify the exploratory data analysis process for multiple network data sets with the help of hierarchical clustering, consensus clustering and heatmaps. Multiple network data consists of multiple disjoint networks that have common variables (e.g. ego networks). This package contains the necessary tools for exploring such data, from the data pre-processing stage to the creation of dynamic visualizations.

The aim is to develop an R package, which is the new.dist package, for the probability (density) function, the distribution function, the quantile function and the associated random number generation function for discrete and continuous distributions, which have recently been proposed in the literature. This package implements the following distributions: The Power Muth Distribution, a Bimodal Weibull Distribution, the Discrete Lindley Distribution, The Gamma-Lomax Distribution, Weighted Geometric Distribution, a Power Log-Dagum Distribution, Kumaraswamy Distribution, Lindley Distribution, the Unit-Inverse Gaussian Distribution, EP Distribution, Akash Distribution, Ishita Distribution, Maxwell Distribution, the Standard Omega Distribution, Slashed Generalized Rayleigh Distribution, Two-Parameter Rayleigh Distribution, Muth Distribution, Uniform-Geometric Distribution, Discrete Weibull Distribution.

Miscellaneous R functions developed as collateral damage over the course of work in statistical and scientific computing for research. These include, for example, utilities that supplement existing idiosyncrasies of the R language, extend existing plotting functionality and aesthetics, help prepare data objects for imputation, and extend access to command line tools and systems-level information.