Modular implementation of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) [Zhang and Li (2007), <DOI:10.1109/TEVC.2007.892759>] for quick assembling and testing of new algorithmic components, as well as easy replication of published MOEA/D proposals. The full framework is documented in a paper published in the Journal of Statistical Software [<doi:10.18637/jss.v092.i06>].

This package provides a collection of matrix functions for teaching and learning matrix linear algebra as used in multivariate statistical methods. Many of these functions are designed for tutorial purposes in learning matrix algebra ideas using R. In some cases, functions are provided for concepts available elsewhere in R, but where the function call or name is not obvious. In other cases, functions are provided to show or demonstrate an algorithm. In addition, a collection of functions are provided for drawing vector diagrams in 2D and 3D and for rendering matrix expressions and equations in LaTeX.

Mouse-tracking, the analysis of mouse movements in computerized experiments, is a method that is becoming increasingly popular in the cognitive sciences. The mousetrap package offers functions for importing, preprocessing, analyzing, aggregating, and visualizing mouse-tracking data. An introduction into mouse-tracking analyses using mousetrap can be found in Wulff, Kieslich, Henninger, Haslbeck, & Schulte-Mecklenbeck (2023) <doi:10.31234/osf.io/v685r> (preprint: <https://osf.io/preprints/psyarxiv/v685r>).

To implement a model-averaging approach with different renewal models, with a primary focus on forecasting large earthquakes. Based on six renewal models (i.e., Poisson, Gamma, Log-Logistics, Weibull, Log-Normal and BPT), model-averaged point estimates are calculated using AIC weights. Additionally, both percentile and studentized bootstrapped model-averaged confidence intervals are constructed. In comparison, point and interval estimation from the individual or "best" model (determined via model selection) can be retrieved.

This package implements an interface to the legacy Fortran code from O'Connell and Dobson (1984) <DOI:10.2307/2531148>. Implements Fortran 77 code for the methods developed by Schouten (1982) <DOI:10.1111/j.1467-9574.1982.tb00774.x>. Includes estimates of average agreement for each observer and average agreement for each subject.

This package provides tools for downloading and organizing National Health and Nutrition Examination Survey (NHANES) public-use data files and the National Center for Health Statistics (NCHS) Public-Use Linked Mortality Files (LMF). Supports structured local caching, codebook access, survey-aware merging, and preparation of survival analysis datasets using NHANES-National Death Index (NDI) linked mortality data (follow-up through December 31, 2019). NHANES methodology is described at <https://wwwn.cdc.gov/nchs/nhanes/Default.aspx>.

Partial informational correlation (PIC) is used to identify the meaningful predictors to the response from a large set of potential predictors. Details of methodologies used in the package can be found in Sharma, A., Mehrotra, R. (2014). <doi:10.1002/2013WR013845>, Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). <doi:10.1016/j.envsoft.2016.05.021>, and Mehrotra, R., & Sharma, A. (2006). <doi:10.1016/j.advwatres.2005.08.007>.

This package provides bridge equations with optional autoregressive terms for nowcasting low-frequency macroeconomic variables (e.g. quarterly GDP) from higher-frequency indicators (e.g. monthly retail sales). Handles the ragged-edge problem where different indicators have different publication lags via mixed-frequency alignment. Includes pseudo-real-time evaluation with expanding or rolling windows, and the Diebold-Mariano test for comparing forecast accuracy following Harvey, Leybourne, and Newbold (1997) <doi:10.1016/S0169-2070(96)00719-4>. No API calls; designed to work with data from any source.

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>.

An R wrapper for pulling data from the National Public Transport Access Nodes ('NaPTAN') API (<https://www.api.gov.uk/dft/national-public-transport-access-nodes-naptan-api/#national-public-transport-access-nodes-naptan-api>). This allows users to download NaPTAN transport information, for the full dataset, by ATCO region code, or by name of region.

The number of distinct alleles observed in a DNA mixture is informative of the number of contributors to the mixture. The package provides methods for computing the probability distribution of the number of distinct alleles in a mixture for a given set of allele frequencies. The mixture contributors may be related according to a provided pedigree.

This package provides tools to save nlmixr2 fitted models in a portable format readable outside of nlmixr2 and independent of the package version. nlmixr2 fits and compares nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving uses compiled C code from the rxode2 package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).

This package contains functions to query and visualize the Neuroimaging features associated with genetically regulated gene expression (GReX). The primary utility, neuroimaGene(), relies on a list of user-defined genes and returns a table of neuroimaging features (NIDPs) associated with each gene. This resource is designed to assist in the interpretation of genome-wide and transcriptome-wide association studies that evaluate brain related traits. Bledsoe (2024) <doi:10.1016/j.ajhg.2024.06.002>. In addition there are several visualization functions that generate summary plots and 2-dimensional visualizations of regional brain measures. Mowinckel (2020).

This package provides functions to query databases and notes in Notion', using the official REST API. To learn more about the functionality of the Notion API, see <https://developers.notion.com/>.

Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the rxode2 package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for support functions like preconditioned fits <doi:10.1208/s12248-016-9866-5>, boostrap and stepwise covariate selection.

Nowcasting using the Chain-Ladder method. Supports both non-cumulative delay-based estimation and model-based completeness fitting (e.g., using logistic or Gompertz curves) to predict final counts from partially reported data.

Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of JavaScript in one package.

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 complete and seamless Nonmem simulation interface within R. Turns Nonmem control streams into simulation control streams, executes them with specified simulation input data and returns the results. The simulation is performed by Nonmem', eliminating manual work and risks of re-implementation of models in other tools.

Calculate NOS (node overlap and segregation) and the associated metrics described in Strona and Veech (2015) <doi:10.1111/2041-210X.12395> and Strona et al. (2018) <doi:10.1111/ecog.03447>. The functions provided in the package enable assessment of structural patterns ranging from complete node segregation to perfect nestedness in a variety of network types. In addition, they provide a measure of network modularity.

This package provides neutrosophic analysis of variance (NANOVA) and analysis of covariance (NANCOVA) for Completely Randomized Designs (CRD) and Randomized Complete Block Designs (RCBD) using interval-valued observations. Computes interval sums of squares, mean squares, F-statistics, significance tests, and interval-based least significant difference (LSD) comparisons. When lower and upper observations are identical (crisp data), the methods reduce to the corresponding classical ANOVA and ANCOVA.

Computes the probability density function, the cumulative density function, quantile function, random numbers and measures of inference for the following families exponentiated generalized gull alpha power family, exponentiated gull alpha powerfamily, gull alpha power family.

Calculate Overall Survival or Recurrence-Free Survival for breast cancer patients, using NHS Predict'. The time interval for the estimation can be set up to 15 years, with default at 10. Incremental therapy benefits are estimated for hormone therapy, chemotherapy, trastuzumab, and bisphosphonates. An additional function, suited for SCAN audits, features a more user-friendly version of the code, with fewer inputs, but necessitates the correct standardised inputs. This work is not affiliated with the development of NHS Predict and its underlying statistical model. Details on NHS Predict can be found at: <doi:10.1186/bcr2464>. The web version of NHS Predict': <https://breast.predict.nhs.uk/>. A small dataset of 50 fictional patient observations is provided for the purpose of running examples with the main two functions, and an additional dataset is provided for running example with the dedicated SCAN function.

This package provides methods and tools for forecasting univariate time series using the NARFIMA (Neural AutoRegressive Fractionally Integrated Moving Average) model. It combines neural networks with fractional differencing to capture both nonlinear patterns and long-term dependencies. The NARFIMA model supports seasonal adjustment, Box-Cox transformations, optional exogenous variables, and the computation of prediction intervals. In addition to the NARFIMA model, this package provides alternative forecasting models including NARIMA (Neural ARIMA), NBSTS (Neural Bayesian Structural Time Series), and NNaive (Neural Naive) for performance comparison across different modeling approaches. The methods are based on algorithms introduced by Chakraborty et al. (2025) <doi:10.48550/arXiv.2509.06697>.