Similarity of dissolution profiles is assessed using the similarity factor f2 according to the EMA guideline (European Medicines Agency 2010) "On the investigation of bioequivalence". Dissolution profiles are regarded as similar if the f2 value is between 50 and 100. For the applicability of the similarity factor f2, the variability between profiles needs to be within certain limits. Often, this constraint is violated. One possibility in this situation is to resample the measured profiles in order to obtain a bootstrap estimate of f2 (Shah et al. (1998) <doi:10.1023/A:1011976615750>). Other alternatives are the model-independent non-parametric multivariate confidence region (MCR) procedure (Tsong et al. (1996) <doi:10.1177/009286159603000427>) or the T2-test for equivalence procedure (Hoffelder (2016) <https://www.ecv.de/suse_item.php?suseId=Z|pi|8430>). Functions for estimation of f1, f2, bootstrap f2, MCR / T2-test for equivalence procedure are implemented.

Estimates the conditional association between an exposure and an outcome given covariates. Three methods are implemented: O-estimation, where a nuisance model for the association between the covariates and the outcome is used; E-estimation where a nuisance model for the association between the covariates and the exposure is used, and doubly robust (DR) estimation where both nuisance models are used. In DR-estimation, the estimates will be consistent when at least one of the nuisance models is correctly specified, not necessarily both. For more information, see Zetterqvist and Sjölander (2015) <doi:10.1515/em-2014-0021>.

Use numerical optimization to fit ordinary differential equations (ODEs) to time series data to examine the dynamic relationships between variables or the characteristics of a dynamical system. It can now be used to estimate the parameters of ODEs up to second order, and can also apply to multilevel systems. See <https://github.com/yueqinhu/defit> for details.

Finds the k nearest neighbours in a dataset of specified points, adding the option to wrap certain variables on a torus. The user chooses the algorithm to use to find the nearest neighbours. Two such algorithms, provided by the packages RANN <https://cran.r-project.org/package=RANN>, and nabor <https://cran.r-project.org/package=nabor>, are suggested.

To overcome the memory limitations for fitting linear (LM) and Generalized Linear Models (GLMs) to large data sets, this package implements the Divide and Recombine (D&R) strategy. It basically divides the entire large data set into suitable subsets manageable in size and then fits model to each subset. Finally, results from each subset are aggregated to obtain the final estimate. This package also supports fitting GLMs to data sets that cannot fit into memory and provides methods for fitting GLMs under linear regression, binomial regression, Poisson regression, and multinomial logistic regression settings. Respective models are fitted using different D&R strategies as described by: Xi, Lin, and Chen (2009) <doi:10.1109/TKDE.2008.186>, Xi, Lin and Chen (2006) <doi:10.1109/TKDE.2006.196>, Zuo and Li (2018) <doi:10.4236/ojs.2018.81003>, Karim, M.R., Islam, M.A. (2019) <doi:10.1007/978-981-13-9776-9>.

This package provides the mathematical model described by "Serostatus Testing & Dengue Vaccine Cost-Benefit Thresholds" in <doi:10.1098/rsif.2019.0234>. Using the functions in the package, that analysis can be repeated using sample life histories, either synthesized from local seroprevalence data using other functions in this package (as in the manuscript) or from some other source. The package provides a vignette which walks through the analysis in the publication, as well as a function to generate a project skeleton for such an analysis.

Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models DIFM package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.

This package provides a function toolkit to facilitate reproducible RNA-Seq Differential Gene Expression (DGE) analysis (Law (2015) <doi:10.12688/f1000research.9005.3>). The tools include both analysis work-flow and utility functions: mapping/unit conversion, count normalization, accounting for unknown covariates, and more. This is a complement/cohort to the DGEobj package that provides a flexible container to manage and annotate Differential Gene Expression analysis results.

This package performs Bayesian posterior inference for deep Gaussian processes following Sauer, Gramacy, and Higdon (2023, <doi:10.48550/arXiv.2012.08015>). See Sauer (2023, <http://hdl.handle.net/10919/114845>) for comprehensive methodological details and <https://bitbucket.org/gramacylab/deepgp-ex/> for a variety of coding examples. Models are trained through MCMC including elliptical slice sampling of latent Gaussian layers and Metropolis-Hastings sampling of kernel hyperparameters. Gradient-enhancement and gradient predictions are offered following Booth (2025, <doi:10.48550/arXiv.2512.18066>). Vecchia approximation for faster computation is implemented following Sauer, Cooper, and Gramacy (2023, <doi:10.48550/arXiv.2204.02904>). Optional monotonic warpings are implemented following Barnett et al. (2025, <doi:10.48550/arXiv.2408.01540>). Downstream tasks include sequential design through active learning Cohn/integrated mean squared error (ALC/IMSE; Sauer, Gramacy, and Higdon, 2023), optimization through expected improvement (EI; Gramacy, Sauer, and Wycoff, 2022, <doi:10.48550/arXiv.2112.07457>), and contour location through entropy (Booth, Renganathan, and Gramacy, 2025, <doi:10.48550/arXiv.2308.04420>). Models extend up to three layers deep; a one layer model is equivalent to typical Gaussian process regression. Incorporates OpenMP and SNOW parallelization and utilizes C/C++ under the hood.

To calculate the sensitivity and specificity in the absence of gold standard using the Bayesian method. The Bayesian method can be referenced at Haiyan Gu and Qiguang Chen (1999) <doi:10.3969/j.issn.1002-3674.1999.04.004>.

Concept drift refers to the change in the data distribution or in the relationships between variables over time. drifter calculates distances between variable distributions or variable relations and identifies both types of drift. Key functions are: calculate_covariate_drift() checks distance between corresponding variables in two datasets, calculate_residuals_drift() checks distance between residual distributions for two models, calculate_model_drift() checks distance between partial dependency profiles for two models, check_drift() executes all checks against drift. drifter is a part of the DrWhy.AI universe (Biecek 2018) <arXiv:1806.08915>.

The ts objects in R are managed using a very specific date format (in the form c(2022, 9) for September 2022 or c(2021, 2) for the second quarter of 2021, depending on the frequency, for example). We focus solely on monthly and quarterly series to manage the dates of ts objects. The general idea is to offer a set of functions to manage this date format without it being too restrictive or too imprecise depending on the rounding. This is a compromise between simplicity, precision and use of the basic stats functions for creating and managing time series (ts(), window()). Les objets ts en R sont gérés par un format de date très particulier (sous la forme c(2022, 9) pour septembre 2022 ou c(2021, 2) pour le deuxième trimestre 2021 selon la fréquence par exemple). On se concentre uniquement sur les séries mensuelles et trimestrielles pour gérer les dates des objets ts. Lidée générale est de proposer un ensemble de fonctions pour gérer ce format de date sans que ce soit trop contraignant ou trop imprécis selon les arrondis. Cest un compromis entre simplicité, précision et utilisation des fonctions du package stats de création et de gestion des séries temporelles (ts(), window()).

This package provides a HTML widget that shows differences between files (text, images, and data frames).

This package provides tools to simulate genetic distance matrices, align and compare them via multidimensional scaling (MDS) and Procrustes, and evaluate imputation with the Bootstrapping Evaluation for Structural Missingness Imputation (BESMI) framework. Methods align with Zhu et al. (2025) <doi:10.3389/fpls.2025.1543956> and the associated software resource Zhu (2025) <doi:10.26188/28602953>.

Likelihood-based inference methods with doubly-truncated data are developed under various models. Nonparametric models are based on Efron and Petrosian (1999) <doi:10.1080/01621459.1999.10474187> and Emura, Konno, and Michimae (2015) <doi:10.1007/s10985-014-9297-5>. Parametric models from the special exponential family (SEF) are based on Hu and Emura (2015) <doi:10.1007/s00180-015-0564-z> and Emura, Hu and Konno (2017) <doi:10.1007/s00362-015-0730-y>. The parametric location-scale models are based on Dorre et al. (2021) <doi:10.1007/s00180-020-01027-6>.

In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. Here we use cyclic shift permutations to identify recurrent copy number alterations in a single cohort or recurrent copy number differences in two cohorts based on a common set of genomic markers. Additional functionality is provided to perform downstream analyses, including the creation of summary files and graphics. DiNAMIC.Duo builds upon the original DiNAMIC package of Walter et al. (2011) <doi:10.1093/bioinformatics/btq717> and leverages the theory developed in Walter et al. (2015) <doi:10.1093/biomet/asv046>. An article describing DiNAMIC.Duo by Walter et al. (2022) can be found at <doi: 10.1093/bioinformatics/btac542>.

This package provides a set of functions for the detection of spatial clusters of disease using count data. Bootstrap is used to estimate sampling distributions of statistics.

Estimation and testing methods for dependently truncated data. Semi-parametric methods are based on Emura et al. (2011)<Stat Sinica 21:349-67>, Emura & Wang (2012)<doi:10.1016/j.jmva.2012.03.012>, and Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>. Parametric approaches are based on Emura & Konno (2012)<doi:10.1007/s00362-014-0626-2> and Emura & Pan (2017)<doi:10.1007/s00362-017-0947-z>. A regression approach is based on Emura & Wang (2016)<doi:10.1007/s10463-015-0526-9>. Quasi-independence tests are based on Emura & Wang (2010)<doi:10.1016/j.jmva.2009.07.006>. Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.

Derivative-Free optimization algorithms. These algorithms do not require gradient information. More importantly, they can be used to solve non-smooth optimization problems.

Real life data is muddy, fuzzy and unpredictable. This makes data manipulations tedious and bringing the data to right shape alone is a major chunk of work. Functions in this package help us get an understanding of dataframes to dramatically reduces data coding time.

Computes small-sample degrees of freedom adjustment for heteroskedasticity robust standard errors, and for clustered standard errors in linear regression. See Imbens and Kolesár (2016) <doi:10.1162/REST_a_00552> for a discussion of these adjustments.

Graphical interface for loading datasets in RStudio from all installed (including unloaded) packages, also includes command line interfaces.

DataSHIELD is an infrastructure and series of R packages that enables the remote and non-disclosive analysis of sensitive research data. This DataSHIELD Interface implementation is for analyzing datasets living in the current R session. The purpose of this is primarily for lightweight DataSHIELD analysis package development.

Comparison of the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.