This package provides analysis results and trial simulation functions for the I-SPY Acute Respiratory Disease Syndrome trial based on composite ranked outcomes. The composite ranked outcome is a hierarchical outcome where trial participants are ranked first by 28 day mortality, then ventilator days, then by advanced respiratory support days. A Bayesian win probability approach is used for analysis. Trial design options include group sequential looks for safety, superiority, futility, and adjustment of randomization probabilities.

Analysis of the initialization for numerical optimization of real-valued functions, particularly likelihood functions of statistical models. See <https://loelschlaeger.de/ino/> for more details.

Takes in vivo toxicokinetic concentration-time data and fits parameters of 1-compartment and 2-compartment models for each chemical. These methods are described in detail in "Informatics for Toxicokinetics" (2025).

An implementation of various methods for estimating intrinsic dimension of vector-valued dataset or distance matrix. Most methods implemented are based on different notion of fractal dimension such as the capacity dimension, the box-counting dimension, and the information dimension.

This package implements the adaptive influence-based borrowing framework proposed by Qinwei Yang, Jingyi Li, Peng Wu, and Shu Yang (2026+) in the paper ``Improving Treatment Effect Estimation in Trials through Adaptive Borrowing of External Controls" <doi:10.48550/arXiv.2604.13973> for augmenting Randomized Controlled Trials (RCTs) with External Control (EC) data. This package provides a comprehensive workflow to: (1) quantify the comparability of external control samples using influence scores approximated via the influence function of the M-estimator; (2) construct candidate borrowing subsets and select the optimal subset that minimizes the Mean Squared Error (MSE); and (3) calibrate systematic differences in external outcomes using R-learner methods implemented via Ordinary Least Squares or Kernel Ridge Regression.

This package provides a framework for analysing inbreeding and heterozygosity-fitness correlations (HFCs) based on microsatellite and SNP markers.

Interlinearized glossed texts (IGT) are used in descriptive linguistics for representing a morphological analysis of a text through a morpheme-by-morpheme gloss. InterlineaR provide a set of functions that targets several popular formats of IGT ('SIL Toolbox', EMELD XML') and that turns an IGT into a set of data frames following a relational model (the tables represent the different linguistic units: texts, sentences, word, morphems). The same pieces of software ('SIL FLEX', SIL Toolbox') typically produce dictionaries of the morphemes used in the glosses. InterlineaR provide a function for turning the LIFT XML dictionary format into a set of data frames following a relational model in order to represent the dictionary entries, the sense(s) attached to the entries, the example(s) attached to senses, etc.

After testing for biased treatment assignment in an observational study using an unaffected outcome, the sensitivity analysis is constrained to be compatible with that test. The package uses the optimization software gurobi obtainable from <https://www.gurobi.com/>, together with its associated R package, also called gurobi; see: <https://www.gurobi.com/documentation/7.0/refman/installing_the_r_package.html>. The method is a substantial computational and practical enhancement of a concept introduced in Rosenbaum (1992) Detecting bias with confidence in observational studies Biometrika, 79(2), 367-374 <doi:10.1093/biomet/79.2.367>.

We use the ISR to handle with PCA-based missing data with high correlation, and the DISR to handle with distributed PCA-based missing data. The philosophy of the package is described in Guo G. (2024) <doi:10.1080/03610918.2022.2091779>.

Get image statistics based on processing fluency theory. The functions provide scores for several basic aesthetic principles that facilitate fluent cognitive processing of images: contrast, complexity / simplicity, self-similarity, symmetry, and typicality. See Mayer & Landwehr (2018) <doi:10.1037/aca0000187> and Mayer & Landwehr (2018) <doi:10.31219/osf.io/gtbhw> for the theoretical background of the methods.

Starting from user-supplied institutional data, these scripts transform, aggregate, and reshape the information to produce key-value pair data files that are able to be uploaded to IPEDS (Integrated Postsecondary Education Data System) through their submission portal <https://surveys.nces.ed.gov/ipeds/>. Starting data specifications can be found in the vignettes. Final files are saved locally to a location of the user's choice. User-friendly readable files can also be produced for purposes of data review and validation.

An implementation of randomization-based hypothesis testing for three different estimands in a cluster-randomized encouragement experiment. The three estimands include (1) testing a cluster-level constant proportional treatment effect (Fisher's sharp null hypothesis), (2) pooled effect ratio, and (3) average cluster effect ratio. To test the third estimand, user needs to install Gurobi (>= 9.0.1) optimizer via its R API. Please refer to <https://www.gurobi.com/documentation/9.0/refman/ins_the_r_package.html>.

This package implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic.

This package provides an up-to-date version of the InvaCost database (<doi:10.6084/m9.figshare.12668570>) in R, and several functions to analyse the costs of invasive alien species (<doi:10.1111/2041-210X.13929>).

An implementation of the MaxLFQ algorithm by Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a comprehensive pipeline for processing proteomics data in data-independent acquisition mode (Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>; Pham et al. 2026 <doi:10.1021/acs.jproteome.5c01038>). It offers additional options for protein quantification using the N most intense fragment ions, using all fragment ions, the median polish algorithm by Tukey (1977, ISBN:0201076160), and a robust linear model. In general, the tool can be used to integrate multiple proportional observations into a single quantitative value.

Interesting igraph datasets from Melanie Walsh's sample social network datasets repository <https://github.com/melaniewalsh/sample-social-network-datasets>.

As a sequel to iNEXT', the iNEXT.beta3D package provides functions to compute standardized taxonomic, phylogenetic, and functional diversity (3D) estimates with a common sample size (for alpha and gamma diversity) or sample coverage (for alpha, beta, gamma diversity as well as dissimilarity or turnover indices). Hill numbers and their generalizations are used to quantify 3D and to make multiplicative decomposition (gamma = alpha x beta). The package also features size- and coverage-based rarefaction and extrapolation sampling curves to facilitate rigorous comparison of beta diversity across datasets. See Chao et al. (2023) <doi:10.1002/ecm.1588> for more details.

This package provides functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites [De Caceres & Legendre (2009) <doi:10.1890/08-1823.1>]. Also includes functions to measure species niche breadth using resource categories [De Caceres et al. (2011) <doi:10.1111/J.1600-0706.2011.19679.x>].

This package provides functions to calculate indices used to score immunoglobulin A (IgA) binding of bacteria in IgA sequencing (IgA-Seq) experiments. This includes the original Kau and Palm indices and more recent methods as described in Jackson et al. (2020) <doi:10.1101/2020.08.19.257501>. Additionally the package contains a function to simulate IgA-Seq data and an example experimental data set for method testing.

Models, analyzes, and forecasts financial intraday signals. This package currently supports a univariate state-space model for intraday trading volume provided by Chen (2016) <doi:10.2139/ssrn.3101695>.

This package provides datasets for the book "Introduction to Statistical Data Analysis for the Life Sciences, Second edition" by Ekstrøm and Sørensen (2014).

This package provides a set of functions for the modeling of data derived from the Minidisc Infiltrometer device. It calculates cumulative infiltration and square root of time. Also, it calculates the A parameter based on soil physical properties.

This package implements tests for the identifying assumptions of instrumental variable models, the local exclusion restriction and monotonicity conditions required for local average treatment effect identification. Covers Kitagawa (2015) <doi:10.3982/ECTA11974>, Mourifie and Wan (2017) <doi:10.1162/REST_a_00622>, and Frandsen, Lefgren, and Leslie (2023) <doi:10.1257/aer.20201860>. Includes a one-shot wrapper that runs all applicable tests on a fitted instrumental variable model. Dispatches on fixest and ivreg model objects.

Implementation of the information matrix test for generalized partial credit models.