This package provides conversion among Seurat', SingleCellExperiment', AnnData', and Loom single-cell data representations while preserving expression matrices, cell and feature metadata, and dimensionality reductions when supported by the target format. The package performs alignment and validity checks during conversion and reports unsupported or unavailable components rather than silently reconstructing them.

This package provides functions to design and apply tests that are anytime valid. The functions can be used to design hypothesis tests in the prospective/randomised control trial setting or in the observational/retrospective setting. The resulting tests remain valid under both optional stopping and optional continuation. The current version includes safe t-tests and safe tests of two proportions. For details on the theory of safe tests, see Ly, A, Boehm, Grunwald, Ramdas and van Ravenzwaaij (2024). "Safe Anytime-Valid Inference: Practical maximally flexible sampling designs for experiments based on e-values" (<doi:10.31234/osf.io/h5vae>) and Grunwald, de Heide and Koolen (2024) "Safe Testing" (<doi:10.1093/jrsssb/qkae011>), for details on safe logrank tests see ter Schure, Perez-Ortiz, Ly and Grunwald (2024) "The Anytime-Valid Logrank Test: Error Control under Continuous Monitoring with Unlimited Horizon" (<doi:10.51387/24-NEJSDS65>), and Turner, Ly and Grunwald (2024) "Generic E-variables for exact sequential k-sample tests that allow for optional stopping" (<doi:10.1016/j.jspi.2023.106116>) for details on safe contingency table tests.

This package provides a framework for performing discrete (share-level) simulations of investment strategies. Simulated portfolios optimize exposure to an input signal subject to constraints such as position size and factor exposure. For background see L. Chincarini and D. Kim (2010, ISBN:978-0-07-145939-6) "Quantitative Equity Portfolio Management".

This package implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.

This package provides a simulator for reticulate evolution under a birth-death-hybridization process. Here the birth-death process is extended to consider reticulate Evolution by allowing hybridization events to occur. The general purpose simulator allows the modeling of three different reticulate patterns: lineage generative hybridization, lineage neutral hybridization, and lineage degenerative hybridization. Users can also specify hybridization events to be dependent on a trait value or genetic distance. We also extend some phylogenetic tree utility and plotting functions for networks. We allow two different stopping conditions: simulated to a fixed time or number of taxa. When simulating to a fixed number of taxa, the user can simulate under the Generalized Sampling Approach that properly simulates phylogenies when assuming a uniform prior on the root age.

Take screenshots from R command and locate an image position.

This package provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) <https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.

SMAHP (pronounced as SOO-MAP) is a novel multi-omics framework for causal mediation analysis of high-dimensional proteogenomic data with survival outcomes. The full methodological details can be found in our recent preprint by Ahn S et al. (2025) <doi:10.48550/arXiv.2503.08606>.

Deterministic, group-safe utilities that transform daily environmental exposure series (pollen and spore counts, with support for other time-varying exposures such as ozone and particulate matter) into analysis-ready lagged and moving-average exposure features. Functions validate temporal regularity, assign ISO 8601 weeks and configurable seasons, impute missing daily values transparently, and construct lagged and windowed exposures suitable for environmental epidemiology and public health analyses.

This package implements five factor extraction methods for asset pricing and macroeconomic forecasting: principal component analysis (PCA), partial least squares (PLS), scaled PCA (sPCA) of Huang, Jiang, Li, Tong, and Zhou (2022) <doi:10.1287/mnsc.2021.4020>, the reduced-rank approach (RRA) of He, Huang, Li, and Zhou (2023) <doi:10.1287/mnsc.2022.4563>, and Instrumented PCA (IPCA) of Kelly, Pruitt, and Su (2019) <doi:10.1016/j.jfineco.2019.05.001>.

This package provides comprehensive analytics, reporting, and testing capabilities for systematic review search strategies. The package focuses on validating search performance, generating standardized PRISMA'-compliant reports, and ensuring reproducibility in evidence synthesis. Features include precision-recall analysis, cross-database performance comparison, benchmark validation against gold standards, sensitivity analysis, temporal coverage assessment, automated report generation, and statistical comparison of search strategies. Supports multiple export formats including CSV', Excel', RIS', BibTeX', and EndNote'. Includes tools for duplicate detection, search strategy optimization, cross-validation frameworks, meta-analysis of benchmark results, power analysis for study design, and reproducibility package creation. Optionally connects to PubMed for direct database searching and real-time strategy comparison using the E-utilities API'. Enhanced with bootstrap comparison methods, McNemar test for strategy evaluation, and comprehensive visualization tools for performance assessment. Methods based on Manning et al. (2008) for information retrieval metrics, Moher et al. (2009) for PRISMA guidelines, and Sampson et al. (2006) for systematic review search methodology.

Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".

Unofficial client for Sentry <https://sentry.io>, a self-hosted or cloud-based error-monitoring service. It will inform about errors in real-time, and includes integration with the Plumber package.

This package provides functions to access and collect data from the Brazilian Federal Senate open data API and website. Covers senators, legislative materials, committees, voting records, speeches, provisional measures, vetoes, and legislative agendas, returning results as tidy data frames ready for analysis.

Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts. This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an RStudio gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) <doi:10.1016/j.chemolab.2015.05.012> and Rimal, R., Almøy, T., Sæbø, S. (2018) <doi:10.1016/j.chemolab.2018.02.009>.

Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, <doi:10.48550/arXiv.2412.20509>). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, <http://jmlr.org/papers/v23/20-1104.html>).

Compose multiple dynamic failure rate distributions into series system distributions where the system hazard equals the sum of component hazards. Supports hazard, survival, cumulative distribution function, density, sampling, and maximum likelihood estimation fitting via the dfr_dist() class from flexhaz'. Series distributions implement the dist.structure protocol so structural queries (phi, min_paths, min_cuts, system_signature, structural importance, reliability, dual) and the importance measures from dist.structure work directly on serieshaz objects. Methods for series system reliability follow Barlow and Proschan (1975, ISBN:0898713692).

Generates cell-level cytokine activity estimates using relevant information from gene sets constructed with the CytoSig and the Reactome databases and scored using the modified Variance-adjusted Mahalanobis (VAM) framework for single-cell RNA-sequencing (scRNA-seq) data. CytoSig database is described in: Jiang at al., (2021) <doi:10.1038/s41592-021-01274-5>. Reactome database is described in: Gillespie et al., (2021) <doi:10.1093/nar/gkab1028>. The VAM method is outlined in: Frost (2020) <doi:10.1093/nar/gkaa582>.

This package provides tools to design experiments, compute Sobol sensitivity indices, and summarise stochastic responses inspired by the strategy described by Zhu and Sudret (2021) <doi:10.1016/j.ress.2021.107815>. Includes helpers to optimise toy models implemented in C++, visualise indices with uncertainty quantification, and derive reliability-oriented sensitivity measures based on failure probabilities. It is further detailed in Logosha, Maumy and Bertrand (2022) <doi:10.1063/5.0246026> and (2023) <doi:10.1063/5.0246024> or in Bertrand, Logosha and Maumy (2024) <https://hal.science/hal-05371803>, <https://hal.science/hal-05371795> and <https://hal.science/hal-05371798>.

Stochastic dominance tests help ranking different distributions. The package implements the consistent test for stochastic dominance by Barrett and Donald (2003) <doi:10.1111/1468-0262.00390>. Specifically, it implements Barrett and Donald's Kolmogorov-Smirnov type tests for first- and second-order stochastic dominance based on bootstrapping 2 and 1.

Render R Markdown reports to HTML and display them instantly in a new browser tab from within a shiny application. Replaces file downloads with a seamless in-browser approach.

This package provides functions to install SciViews additions to R, and more tools.

This package provides a stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) <doi:10.1038/s41598-020-79317-8>, Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.

Performance of functional kriging, cokriging, optimal sampling and simulation for spatial prediction of functional data. The framework of spatial prediction, optimal sampling and simulation are extended from scalar to functional data. SpatFD is based on the Karhunen-Loève expansion that allows to represent the observed functions in terms of its empirical functional principal components. Based on this approach, the functional auto-covariances and cross-covariances required for spatial functional predictions and optimal sampling, are completely determined by the sum of the spatial auto-covariances and cross-covariances of the respective score components. The package provides new classes of data and functions for modeling spatial dependence structure among curves. The spatial prediction of curves at unsampled locations can be carried out using two types of predictors, and both of them report, the respective variances of the prediction error. In addition, there is a function for the determination of spatial locations sampling configuration that ensures minimum variance of spatial functional prediction. There are also two functions for plotting predicted curves at each location and mapping the surface at each time point, respectively. References Bohorquez, M., Giraldo, R., and Mateu, J. (2016) <doi:10.1007/s10260-015-0340-9>, Bohorquez, M., Giraldo, R., and Mateu, J. (2016) <doi:10.1007/s00477-016-1266-y>, Bohorquez M., Giraldo R. and Mateu J. (2021) <doi:10.1002/9781119387916>.