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>).

Utilities and kinship information for behavior genetics and developmental research using the National Longitudinal Survey of Youth (NLSY; <https://www.nlsinfo.org/>).

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.

Natural strata can be used in observational studies to balance the distributions of many covariates across any number of treatment groups and any number of comparisons. These strata have proportional amounts of units within each stratum across the treatments, allowing for simple interpretation and aggregation across strata. Within each stratum, the units are chosen using randomized rounding of a linear program that balances many covariates. For more details, see Brumberg et al. (2022) <doi:10.1111/rssa.12848> and Brumberg et al.(2023) <doi:10.1093/jrsssc/qlad010>. To solve the linear program, the Gurobi commercial optimization software is recommended, but not required. The gurobi R package can be installed by following the instructions at <https://docs.gurobi.com/projects/optimizer/en/current/reference/r/setup.html> after claiming your free academic license at <https://www.gurobi.com/academia/academic-program-and-licenses/>.

This package provides utility functions, distributions, and fitting methods for Bayesian Spatial Capture-Recapture (SCR) and Open Population Spatial Capture-Recapture (OPSCR) modelling using the nimble package (de Valpine et al. 2017 <doi:10.1080/10618600.2016.1172487 >). Development of the package was motivated primarily by the need for flexible and efficient analysis of large-scale SCR data (Bischof et al. 2020 <doi:10.1073/pnas.2011383117 >). Computational methods and techniques implemented in nimbleSCR include those discussed in Turek et al. 2021 <doi:10.1002/ecs2.3385>; among others. For a recent application of nimbleSCR, see Milleret et al. (2021) <doi:10.1098/rsbl.2021.0128>.

Mainly for maximum likelihood estimation of nonparametric and semiparametric mixture models, but can also be used for fitting finite mixtures. The algorithms are developed in Wang (2007) <doi:10.1111/j.1467-9868.2007.00583.x> and Wang (2010) <doi:10.1007/s11222-009-9117-z>.

Optimization for nonlinear objective and constraint functions. Linear or nonlinear equality and inequality constraints are allowed. It accepts the input parameters as a constrained matrix.

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

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.

This package provides satellite tracking data from nomadic pectoral sandpipers published in Kempenaers and Valcu (2017) <doi:10.1038/nature20813>. The data can also serve as benchmark data for clustering movement tracks.

Adds brute force and multiple starting values to nls.

Adapts the index-flood technique to extreme air temperature data. The package uses the additive approach proposed in Martins et al. (2022) <doi:10.1590/1678-4499.20220061> and adapt it to climate change conditions through the use of nonstationary parametric distributions.

This package provides a toolkit for medical records data analysis. The naryn package implements an efficient data structure for storing medical records, and provides a set of functions for data extraction, manipulation and analysis.

This package provides functions for working with NHS number checksums. The UK's National Health Service issues NHS numbers to all users of its services and this package implements functions for verifying that the numbers are valid according to the checksum scheme the NHS use. Numbers can be validated and checksums created.

This package provides a suite of tools that can assist in enhancing the processing efficiency of SQL and R scripts. - The libr_unused() retrieves a vector of package names that are called within an R script but are never actually used in the script. - The libr_used() retrieves a vector of package names actively utilized within an R script; packages loaded using library() but not actually used in the script will not be included. - The libr_called() retrieves a vector of all package names which are called within an R script. - nolock() appends WITH (nolock) to all tables in SQL queries. This facilitates reading from databases in scenarios where non-blocking reads are preferable, such as in high-transaction environments.

This package provides tools to generate Necklaces, Bracelets, Lyndon words and de Bruijn sequences. The generation relies on integer partitions and uses the KStatistics package. Methods used in the package refers to E. Di Nardo and G. Guarino (2022) <doi:10.48550/arXiv.2208.06855>.

Nonparametric smoothing methods for density and regression estimation and inference with circular data. The package provides kernel density estimation along with inferential tools such as circular SiZer for feature significance, mode estimation, and modal clustering. It includes multiple methods for selecting the smoothing parameter, allowing users to optimize the trade-off between bias and variance. Various plotting functions help visualize estimated densities, modes, clusters, and significance features. For regression, the package implements nonparametric estimation of the mean regression function as well as other conditional characteristics, including modal regression and generalized regression. Bandwidth selection is also supported in the regression context, and testing procedures are available to assess structural features or effects in circular regression models.

This package provides efficient methods to compute co-occurrence matrices, pointwise mutual information (PMI) and singular value decomposition (SVD). In the biomedical and clinical settings, one challenge is the huge size of databases, e.g. when analyzing data of millions of patients over tens of years. To address this, this package provides functions to efficiently compute monthly co-occurrence matrices, which is the computational bottleneck of the analysis, by using the RcppAlgos package and sparse matrices. Furthermore, the functions can be called on SQL databases, enabling the computation of co-occurrence matrices of tens of gigabytes of data, representing millions of patients over tens of years. Partly based on Hong C. (2021) <doi:10.1038/s41746-021-00519-z>.

This package provides functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.

Calculate the precision in mean differences (raw or Cohen's D) and correlation coefficients for different sample sizes. Uses permutations of the collected functional magnetic resonance imaging (fMRI) region of interest data. Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024) <doi:10.31234/osf.io/cz32t>.

This package provides tools to support research on vowel covariation. Methods are provided to support Principal Component Analysis workflows (as in Brand et al. (2021) <doi:10.1016/j.wocn.2021.101096> and Wilson Black et al. (2023) <doi:10.1515/lingvan-2022-0086>).

This package provides functions for specifying and fitting nested dichotomy logistic regression models for a multi-category response and methods for summarising and plotting those models. Nested dichotomies are statistically independent, and hence provide an additive decomposition of tests for the overall polytomous response. When the dichotomies make sense substantively, this method can be a simpler alternative to the standard multinomial logistic model which compares response categories to a reference level. See: J. Fox (2016), "Applied Regression Analysis and Generalized Linear Models", 3rd Ed., ISBN 1452205663.

Optimizing regular numeric problems in optically stimulated luminescence dating, such as: equivalent dose calculation, dose rate determination, growth curve fitting, decay curve decomposition, statistical age model optimization, and statistical plot visualization.

This package provides nonparametric CUSUM tests for detecting changes in possibly serially dependent univariate or low-dimensional multivariate observations. Retrospective tests sensitive to changes in the expectation, the variance, the covariance, the autocovariance, the distribution function, Spearman's rho, Kendall's tau, Gini's mean difference, and the copula are provided, as well as a test for detecting changes in the distribution of independent block maxima (with environmental studies in mind). The package also contains a test sensitive to changes in the autocopula and a combined test of stationarity sensitive to changes in the distribution function and the autocopula. The latest additions are an open-end sequential test based on the retrospective CUSUM statistic that can be used for monitoring changes in the mean of possibly serially dependent univariate observations, as well as closed-end and open-end sequential tests based on empirical distribution functions that can be used for monitoring changes in the contemporary distribution of possibly serially dependent univariate or low-dimensional multivariate observations.