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 provides different interval estimates of a location parameter when the sample size is one or more. These include classical methods when n=1, new Bayesian analogues of these classical methods, and extensions of these methods to larger sample sizes in the normal case. Other functions calculate Bayes factors based on t-statistics, and implement the (generalized) inverse normal distribution. These methods are described in Gerard (2026) <doi:10.48550/arXiv.2607.25007>.

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> .

Format numbers and plots for publication; includes the removal of leading zeros, standardization of number of digits, addition of affixes, and a p-value formatter. These tools combine the functionality of several base functions such as paste()', format()', and sprintf() into specific use case functions that are named in a way that is consistent with usage, making their names easy to remember and easy to deploy.

This package provides a set of functions to scrape and analyze rugby data. Supports competitions including the National Rugby League, New South Wales Cup, Queensland Cup, Super League, and various representative and women's competitions. Includes functions to fetch player statistics, match results, ladders, venues, and coaching data. Designed to assist analysts, fans, and researchers in exploring historical and current rugby league data. See Woods et al. (2017) <doi:10.1123/ijspp.2016-0187> for an example of rugby league performance analysis methodology.

This package provides several novel exact hypothesis tests with minimal assumptions on the errors. The tests are exact, meaning that their p-values are correct for the given sample sizes (the p-values are not derived from asymptotic analysis). The test for stochastic inequality is for ordinal comparisons based on two independent samples and requires no assumptions on the errors. The other tests include tests for the mean and variance of a single sample and comparing means in independent samples. All these tests only require that the data has known bounds (such as percentages that lie in [0,100]. These bounds are part of the input.

Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors (including random intercepts), interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for diagnostics, inference, and prediction.

This package provides a computational toolkit for analyzing nematode communities in ecological studies. Includes methods to quantify nematode-based ecological indicators such as metabolic footprints, energy flow metrics, and community structure. These tools support assessments of soil health, ecosystem functioning, and trophic interactions, standardizing the use of nematodes as bioindicators.

Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited. Seven normality test are implemented. The asymptotic Lobato & Velasco's, asymptotic Epps, Psaradakis and Vávra, Lobato & Velasco's and Epps sieve bootstrap approximations, El bouch et al., and the random projections tests for univariate stationary process. Some other diagnostics such as, unit root test for stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. Additionally, the El bouch test performs normality tests for bivariate time series. The package also offers residual diagnostic for linear time series models developed in several packages.

This package creates quasi-proportional Venn diagrams with an arbitrary number of sets. It is related to the old nVennR package, but the algorithm and use have been reworked.

This package provides functions for Bayesian analysis of data from randomized experiments with non-compliance. The functions are based on the models described in Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>. Currently only two types of outcome models are supported: binary outcomes and normally distributed outcomes. Models can be fit with and without the exclusion restriction and/or the strong access monotonicity assumption. Models are fit using the data augmentation algorithm as described in Tanner and Wong (1987) <doi:10.2307/2289457>.

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

Computes the pdf, cdf, quantile function and generating random numbers for neutrosophic distributions. This family have been developed by different authors in the recent years. See Patro and Smarandache (2016) <doi:10.5281/zenodo.571153> and Rao et al (2023) <doi:10.5281/zenodo.7832786>.

Estimators and variance estimators tailored to the NILS hierarchical design (Adler et al. 2020, <https://res.slu.se/id/publ/105630>; Grafström et al. 2023, <https://res.slu.se/id/publ/128235>). The National Inventories of Landscapes in Sweden (NILS) is a long-term national monitoring program that collects, analyses and presents data on Swedish nature, covering both common and rare habitats <https://www.slu.se/om-slu/organisation/institutioner/skoglig-resurshushallning/miljoanalys/nils/>.

To estimate ecological stochasticity in community assembly. Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. The new index, normalized stochasticity ratio (NST), is to estimate ecological stochasticity, i.e. relative importance of stochastic processes, in community assembly. With functions in this package, NST can be calculated based on different similarity metrics and/or different null model algorithms, as well as some previous indexes, e.g. previous Stochasticity Ratio (ST), Standard Effect Size (SES), modified Raup-Crick metrics (RC). Functions for permutational test and bootstrapping analysis are also included. Previous ST is published by Zhou et al (2014) <doi:10.1073/pnas.1324044111>. NST is modified from ST by considering two alternative situations and normalizing the index to range from 0 to 1 (Ning et al 2019) <doi:10.1073/pnas.1904623116>. A modified version, MST, is a special case of NST, used in some recent or upcoming publications, e.g. Liang et al (2020) <doi:10.1016/j.soilbio.2020.108023>. SES is calculated as described in Kraft et al (2011) <doi:10.1126/science.1208584>. RC is calculated as reported by Chase et al (2011) <doi:10.1890/ES10-00117.1> and Stegen et al (2013) <doi:10.1038/ismej.2013.93>. Version 3 added NST based on phylogenetic beta diversity, used by Ning et al (2020) <doi:10.1038/s41467-020-18560-z>.

This package provides functions and datasets to support the book by Galecki and Burzykowski (2013), Linear Mixed-Effects Models: A Step-by-Step Approach', Springer. Includes functions for power calculations, log-likelihood contributions, and data simulation for linear mixed-effects models.

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>.

This R package provides a calculation of between-cases AUC estimate, corresponding covariance, and variance estimate in the nested data problem. Also, the package has the function to simulate the nested data. The calculated between-cases AUC estimate is used to evaluate the reader's diagnostic performance in clinical tasks with nested data. For more details on the above methods, please refer to the paper by H Du, S Wen, Y Guo, F Jin, BD Gallas (2022) <doi:10.1177/09622802221111539>.

Neighbour-balanced designs ensure that no treatment is disadvantaged unfairly by its surroundings. The treatment allocation in these designs is such that every treatment appears equally often as a neighbour with every other treatment. Neighbour Balanced Designs are employed when there is a possibility of neighbour effects from treatments used in adjacent experimental units. In the literature, a vast number of such designs have been developed. This package generates some efficient neighbour balanced block designs which are balanced and partially variance balanced for estimating the contrast pertaining to direct and neighbour effects, as well as provides a function for analysing the data obtained from such trials (Azais, J.M., Bailey, R.A. and Monod, H. (1993). "A catalogue of efficient neighbour designs with border plots". Biometrics, 49, 1252-1261 ; Tomar, J. S., Jaggi, Seema and Varghese, Cini (2005)<DOI: 10.1080/0266476042000305177>. "On totally balanced block designs for competition effects"). This package contains functions named nbbd1(),nbbd2(),nbbd3(),pnbbd1() and pnbbd2() which generates neighbour balanced block designs within a specified range of number of treatment (v). It contains another function named anlys()for performing the analysis of data generated from such trials.

This package provides a program for Bayesian analysis of univariate normal mixtures with an unknown number of components, following the approach of Richardson and Green (1997) <doi:10.1111/1467-9868.00095>. This makes use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter sub-spaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution.

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

Calculating the density, cumulative distribution, quantile, and random number of neo-normal distribution. It also interfaces with the brms package, allowing the use of the neo-normal distribution as a custom family. This integration enables the application of various brms formulas for neo-normal regression. Modified to be Stable as Normal from Burr (MSNBurr), Modified to be Stable as Normal from Burr-IIa (MSNBurr-IIa), Generalized of MSNBurr (GMSNBurr), Jones-Faddy Skew-t, Fernandez-Osiewalski-Steel Skew Exponential Power, and Jones Skew Exponential Power distributions are supported. References: Choir, A. S. (2020).Unpublished Dissertation, Iriawan, N. (2000).Unpublished Dissertation, Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z., & Bastiani, F. D. (2019) <doi:10.1201/9780429298547>.

This package provides a unified set of helper functions to access datasets from the NYC Open Data platform <https://opendata.cityofnewyork.us/>. Functions return results as tidy tibbles and support optional filtering, sorting, and row limits via the Socrata API. The package includes endpoints for 311 service requests, DOB job applications, juvenile justice metrics, school safety, environmental data, event permitting, and additional citywide datasets.

Download and parse model outputs from the U.S. Geological Survey (USGS) National Water Availability Assessment (NWAA) Data Companion web services. Provides typed, validated wrappers for the published NWAA models across the Water Use, Water Quantity, and Integrated Water Availability families, returning results at HUC12 spatial resolution.