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.

This package provides helpers for discovering, planning, and downloading open-access datasets from the Neuroscience Multi-Omic Archive ('NeMO'; <https://nemoarchive.org/>). The package builds reproducible file manifests that record search parameters, file metadata, download URLs, checksums, and local file paths. It supports exploratory NeMO metadata queries and provides first-pass bridges from downloaded files into SingleCellExperiment and Seurat workflows.

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

Imputation for both missing covariates and censored observations (optional) for survival data with missing covariates by the nearest neighbor based multiple imputation algorithm as described in Hsu et al. (2006) <doi:10.1002/sim.2452>, and Hsu and Yu (2018) <doi: 10.1177/0962280218772592>. Note that the current version can only impute for a situation with one missing covariate.

Routines for fitting and simulating data under autoregressive fractionally integrated moving average (ARFIMA) models, without the constraint of covariance stationarity. Two fitting methods are implemented, a pseudo-maximum likelihood method and a minimum distance estimator. Mayoral, L. (2007) <doi:10.1111/j.1368-423X.2007.00202.x>. Beran, J. (1995) <doi:10.1111/j.2517-6161.1995.tb02054.x>.

This package provides a set of functions providing several outlier (i.e., studies with extreme findings) and influential detection measures and methodologies in network meta-analysis : - simple outlier and influential detection measures - outlier and influential detection measures by considering study deletion (shift the mean) - plots for outlier and influential detection measures - Q-Q plot for network meta-analysis - Forward Search algorithm in network meta-analysis. - forward plots to monitor statistics in each step of the forward search algorithm - forward plots for summary estimates and their confidence intervals in each step of forward search algorithm.

This package provides color palettes that approximate the University of Notre Dame brand colors, together with ggplot2 discrete color and fill scales. The default palette leads with six Notre Dame brand colors that read clearly on a white background and extends through seven former Notre Dame brand colors, interpolating when more colors are needed than the brand supplies. The palette is intended for statistical visualization and psychometric analysis: the white-safe ordering keeps plotted categories legible, and the near-white-to-navy ramps suit continuous quantities such as correlations and factor loadings. A colorblind-friendly ordering of the Notre Dame colors themselves (arranged so the colors stay distinguishable under simulated deuteranopia, protanopia, and tritanopia), the former colors as a standalone palette, the four near-white brand tints (plus six informal soft backgrounds) for backgrounds and sequential ramps, a reference table of every color with brand and role labels, and a palette-preview helper are also provided. A matching R Markdown stylesheet, built from the same colors so a report and its figures share one brand palette, themes HTML and shiny output. The colors approximate those described in the University's branding guidelines (<https://onmessage.nd.edu/university-branding/colors/>). This is an independent project and is not affiliated with or endorsed by the University of Notre Dame.

Macros to generate nimble code from a concise syntax. Included are macros for generating linear modeling code using a formula-based syntax and for building for() loops. For more details review the nimble manual: <https://r-nimble.org/manual/cha-user-defined.html#sec:user-macros>.

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.

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

Systematically creates and modifies NONMEM(R) control streams. Harvests NONMEM output, builds run logs, creates derivative data, generates diagnostics. NONMEM (ICON Development Solutions <https://www.iconplc.com/>) is software for nonlinear mixed effects modeling. See package?nonmemica'.

Novel responsive tools for developing R based Shiny dashboards and applications. The scripts and style sheets are based on jQuery <https://jquery.com/> and Bootstrap <https://getbootstrap.com/>.

Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods. Exact procedures are performed when computationally possible. Monte Carlo and Asymptotic procedures are performed otherwise. For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.

This package provides a collection of statistical tools for objective (non-supervised) applications of the Regional Frequency Analysis methods in hydrology. The package refers to the index-value method and, more precisely, helps the hydrologist to: (1) regionalize the index-value; (2) form homogeneous regions with similar growth curves; (3) fit distribution functions to the empirical regional growth curves. Most of the methods are those described in the Flood Estimation Handbook (Centre for Ecology & Hydrology, 1999, ISBN:9781906698003). Homogeneity tests from Hosking and Wallis (1993) <doi:10.1029/92WR01980> and Viglione et al. (2007) <doi:10.1029/2006WR005095> are available.

Measure the dependence structure between two random variables with a new correlation coefficient and extend it to hypothesis test, feature screening and false discovery rate control.

Estimation, inference, bandwidth selection, and graphical procedures for kernel density and local polynomial regression methods, including robust bias-corrected confidence intervals as described in Calonico, Cattaneo and Farrell (2018, <doi:10.1080/01621459.2017.1285776>). The package includes lprobust() for local polynomial point estimation and robust bias-corrected inference, lpbwselect() for local polynomial bandwidth selection, kdrobust() for kernel density point estimation and robust bias-corrected inference, kdbwselect() for kernel density bandwidth selection, and nprobust.plot() for plotting results. The main methodological and numerical features are described in Calonico, Cattaneo and Farrell (2019, <doi:10.18637/jss.v091.i08>).

Package including an interactive Shiny application for testing normality visually.

Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.

Includes five particle filtering algorithms for use with state space models in the nimble system: Auxiliary', Bootstrap', Ensemble Kalman filter', Iterated Filtering 2', and Liu-West', as described in Michaud et al. (2021), <doi:10.18637/jss.v100.i03>. A full User Manual is available at <https://r-nimble.org>.

Many tools for calculating network, node, or tie marks, measures, motifs and memberships of many different types of networks. Marks identify structural positions, measures quantify network properties, memberships classify nodes into groups, and motifs tabulate substructure participation. All functions operate with all classes of network data covered in manynet', and on directed, undirected, multiplex, multimodal, signed, and other networks.

Ships statistical and mathematical routines from R internal nmath ('Mathlib') as OpenCL C sources under directory inst/cl/', with R wrappers that use the GPU when OpenCL is available at compile time and fall back to stats equivalents otherwise. Aimed at package developers building custom kernels (for example Bayesian GLMs via suggested package glmbayes') using opencltools kernel loaders and related helpers. Contains translated shims, an illustrative GLM-related kernel subsystem, vignettes, and optional GPU acceleration. The ported routines are translated from the nmath ('Mathlib') and Rmath sources of R Core Team (2026) "R: A Language and Environment for Statistical Computing" <doi:10.32614/R.manuals>. OpenCL GPU execution follows the standard described in Stone, Gohara, and Shi (2010) <doi:10.1109/MCSE.2010.69>. The likelihood subgradient simulation methodology implemented by the illustrative GLM kernel subsystem is described in Nygren and Nygren (2006) <doi:10.1198/016214506000000357>.

Statistical methods for whole-trial and time-domain analysis of single cell neural response to multiple stimuli presented simultaneously. The package is based on the paper by C Glynn, ST Tokdar, A Zaman, VC Caruso, JT Mohl, SM Willett, and JM Groh (2021) "Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure", is in press for publication by the Annals of Applied Statistics. A preprint may be found at <arXiv:1911.04387>.

In this implementation of the Naive Bayes classifier following class conditional distributions are available: Bernoulli', Categorical', Gaussian', Poisson', Multinomial and non-parametric representation of the class conditional density estimated via Kernel Density Estimation. Implemented classifiers handle missing data and can take advantage of sparse data.

Estimating the first and second derivatives of a regression function by the method of Wang and Lin (2015) <http://www.jmlr.org/papers/v16/wang15b.html>.