Estimate complex Structural Equation Models (SEMs) by fitting Partial Least Squares Structural Equation Modeling (PLS-SEM) and Partial Least Squares consistent Structural Equation Modeling (PLSc-SEM) specifications that handle categorical data, non-linear relations, and multilevel structures. The implementation follows Lohmöller (1989) for the classic PLS-SEM algorithm, Dijkstra and Henseler (2015) for consistent PLSc-SEM, Dijkstra et al., (2014) for nonlinear PLSc-SEM, and Schuberth, Henseler, Dijkstra (2018) for ordinal PLS-SEM and PLSc-SEM. Additional extensions are under development. The MC-OrdPLSc algorithm, used to handle ordinal interaction models is detailed in Slupphaug et al., (2026). References: Lohmöller, J.-B. (1989, ISBN:9783790803002). "Latent Variable Path Modeling with Partial Least Squares." Dijkstra, T. K., & Henseler, J. (2015). <doi:10.1016/j.jmva.2015.06.002>. "Consistent partial least squares path modeling." Dijkstra, T. K., & Schermelleh-Engel, K. (2014). <doi:10.1016/j.csda.2014.07.008>. "Consistent partial least squares for nonlinear structural equation models." Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018). <doi:10.1007/s11135-018-0767-9>. "Partial least squares path modeling using ordinal categorical indicators." Slupphaug, K. Mehmetoglu, M. & Mittner, M. (2026). <doi:10.31234/osf.io/fwzj6_v1>. "Consistent Estimates from Biased Estimators: Monte-Carlo Consistent Partial Least Squares for Latent Interaction Models with Ordinal Indicators.".

partitionMetric computes a distance between two partitions of a set.

Tests for a comparison of two partially overlapping samples. A comparison of means using the partially overlapping samples t-test: See Derrick, Russ, Toher and White (2017), Test statistics for the comparison of means for two samples which include both paired observations and independent observations, Journal of Modern Applied Statistical Methods, 16(1). A comparison of proportions using the partially overlapping samples z-test: See Derrick, Dobson-Mckittrick, Toher and White (2015), Test statistics for comparing two proportions with partially overlapping samples. Journal of Applied Quantitative Methods, 10(3).

This package implements the softmax aggregation method for calculating Plant Stress Response Index (PSRI) from time-series germination data under environmental stressors including prions, xenobiotics, osmotic stress, heavy metals, and chemical contaminants. Provides zero-robust PSRI computation through adaptive softmax weighting of germination components (Maximum Stress-adjusted Germination, Maximum Rate of Germination, complementary Mean Time to Germination, and Radicle Vigor Score), eliminating the zero-collapse failure mode of the geometric mean approach implemented in PSRICalc'. Includes perplexity-based temperature parameter calibration and modular component functions for transparent germination analysis. Built on the methodological foundation of the Osmotic Stress Response Index (OSRI) framework developed by Walne et al. (2020) <doi:10.1002/agg2.20087>. Note: This package implements methodology currently under peer review. Please contact the author before publication using this approach. Development followed an iterative human-machine collaboration where all algorithmic design, statistical methodologies, and biological validation logic were conceptualized, tested, and iteratively refined by Richard A. Feiss through repeated cycles of running experimental data, evaluating analytical outputs, and selecting among candidate algorithms and approaches. AI systems (Anthropic Claude and OpenAI GPT) served as coding assistants and analytical sounding boards under continuous human direction. The selection of statistical methods, evaluation of biological plausibility, and all final methodology decisions were made by the human author. AI systems did not independently originate algorithms, statistical approaches, or scientific methodologies.

This package implements a unified interface for benchmarking meta-analytic publication bias correction methods through simulation studies (see BartoÅ¡ et al., 2025, <doi:10.48550/arXiv.2510.19489>). It provides 1) predefined data-generating mechanisms from the literature, 2) functions for running meta-analytic methods on simulated data, 3) pre-simulated datasets and pre-computed results for reproducible benchmarks, 4) tools for visualizing and comparing method performance.

Implementation of PsychroLib <https://github.com/psychrometrics/psychrolib> library which contains functions to enable the calculation properties of moist and dry air in both metric (SI) and imperial (IP) systems of units. References: Meyer, D. and Thevenard, D (2019) <doi:10.21105/joss.01137>.

Fits right-truncated meta-analysis (RTMA), a bias correction for the joint effects of p-hacking (i.e., manipulation of results within studies to obtain significant, positive estimates) and traditional publication bias (i.e., the selective publication of studies with significant, positive results) in meta-analyses [see Mathur MB (2022). "Sensitivity analysis for p-hacking in meta-analyses." <doi:10.31219/osf.io/ezjsx>.]. Unlike publication bias alone, p-hacking that favors significant, positive results (termed "affirmative") can distort the distribution of affirmative results. To bias-correct results from affirmative studies would require strong assumptions on the exact nature of p-hacking. In contrast, joint p-hacking and publication bias do not distort the distribution of published nonaffirmative results when there is stringent p-hacking (e.g., investigators who hack always eventually obtain an affirmative result) or when there is stringent publication bias (e.g., nonaffirmative results from hacked studies are never published). This means that any published nonaffirmative results are from unhacked studies. Under these assumptions, RTMA involves analyzing only the published nonaffirmative results to essentially impute the full underlying distribution of all results prior to selection due to p-hacking and/or publication bias. The package also provides diagnostic plots described in Mathur (2022).

Build Plumber APIs that can be used in Tableau workbooks. Annotations in R comments allow APIs to conform to the Tableau Analytics Extension specification, so that R code can be used to power Tableau workbooks.

This package provides a comprehensive framework for planning and executing analyses in R. It provides a structured approach to running the same function multiple times with different arguments, executing multiple functions on the same datasets, and creating systematic analyses across multiple strata or variables. The framework is particularly useful for applying the same analysis across multiple strata (e.g., locations, age groups), running statistical methods on multiple variables (e.g., exposures, outcomes), generating multiple tables or graphs for reports, and creating systematic surveillance analyses. Key features include efficient data management, structured analysis planning, flexible execution options, built-in debugging tools, and hash-based caching.

Create the density contour plot for bivariate inverse Gaussian distribution for given non negative random variables.

Pre-testing and diagnostic tools for panel data analysis. Researchers should run these tests before any panel regression to verify modelling assumptions. The package implements: (1) the Hsiao (2014, <ISBN:978-1-107-65763-2>) homogeneity F-tests (F1/F2/F3), Swamy (1970) <doi:10.2307/1913012> parameter heterogeneity test, and Pesaran (2004) <doi:10.2139/ssrn.572504> cross-sectional dependence test via xtpretest(); (2) missing-data detection, mechanism testing, and imputation for unbalanced panels via xtmispanel(); (3) quantile-regression cross-sectional dependence tests (T_tau and T-tilde_tau statistics) of Demetrescu, Hosseinkouchack and Rodrigues (2023) <doi:10.1016/j.jeconom.2022.09.001> via xtcsdq(); and (4) the panel quantile-regression slope homogeneity S-hat and D-hat statistics of Galvao, Juhl, Montes-Rojas and Olmo (2017) <doi:10.1080/07350015.2015.1054493> via xtqsh(). Together these tests address three fundamental pre-testing questions: (i) are slopes homogeneous? (ii) is there cross-sectional dependence? and (iii) is the panel balanced and is missingness ignorable?

This package provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Hahsler and Cassandra <doi:10.32614/RJ-2024-021>.

Quantification of variation in organismal color patterns as obtained from image data. Patternize defines homology between pattern positions across images either through fixed landmarks or image registration. Pattern identification is performed by categorizing the distribution of colors using RGB thresholds or image segmentation.

Packages data about the victims of the Pinochet regime as compiled by the Chilean National Commission for Truth and Reconciliation Report (1991, ISBN:9780268016463).

This package provides access to a high performant random distribution sampler for the Polya Gamma Distribution using either C++ headers for Rcpp or RcppArmadillo and R'.

Reproducible, programmatic retrieval of survey datasets from the Pew Research Center.

Normalization of numerical matrices by minimizing the mean/median/mode difference between all column pairs.

This package provides a toolbox for deterministic, probabilistic and privacy-preserving record linkage techniques. Combines the functionality of the Merge ToolBox (<https://www.record-linkage.de>) with current privacy-preserving techniques.

Analyzis and filtering of phylogenomics datasets. It takes an input either a collection of gene trees (then transformed to matrices) or directly a collection of gene matrices and performs an iterative process to identify what species in what genes are outliers, and whose elimination significantly improves the concordance between the input matrices. The methods builds upon the Distatis approach (Abdi et al. (2005) <doi:10.1101/2021.09.08.459421>), a generalization of classical multidimensional scaling to multiple distance matrices.

Estimates principal causal effects under principal stratification using a margin-free, conditional odds ratio sensitivity parameter. This framework unifies the monotonicity assumption and the counterfactual intermediate independence assumption, allowing for robust analysis when monotonicity may not hold. Computes point estimates, standard errors, and confidence intervals for conditionally doubly robust and debiased machine learning estimators. The methodological details are described in Tong, Kahan, Harhay, and Li (2025) <doi:10.48550/arXiv.2501.17514>.

This wrapper houses PathLit API endpoints for R. The usage of these endpoints require the use of an API key which can be obtained at <https://www.pathlit.io/docs/cli/>.

Allows biomechanical pressure data from a range of systems to be imported and processed in a reproducible manner. Automatic and manual tools are included to let the user define regions (masks) to be analyzed. Also includes functions for visualizing and animating pressure data. Example methods are described in Shi et al., (2022) <doi:10.1038/s41598-022-19814-0>, Lee et al., (2014) <doi:10.1186/1757-1146-7-18>, van der Zward et al., (2014) <doi:10.1186/1757-1146-7-20>, Najafi et al., (2010) <doi:10.1016/j.gaitpost.2009.09.003>, Cavanagh and Rodgers (1987) <doi:10.1016/0021-9290(87)90255-7>.

This package provides an easy-to-use yet adaptable set of tools to conduct person-center analysis using a two-step clustering procedure. As described in Bergman and El-Khouri (1999) <DOI:10.1002/(SICI)1521-4036(199910)41:6%3C753::AID-BIMJ753%3E3.0.CO;2-K>, hierarchical clustering is performed to determine the initial partition for the subsequent k-means clustering procedure.

Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.