An implementation of chunked, compressed, N-dimensional arrays for R. Zarr spec V2 (2024) <doi:10.5281/zenodo.11320255>.

Implementation of the Phoenix and Phoenix-8 Sepsis Criteria as described in "Development and Validation of the Phoenix Criteria for Pediatric Sepsis and Septic Shock" by Sanchez-Pinto, Bennett, DeWitt, Russell et al. (2024) <doi:10.1001/jama.2024.0196> (Drs. Sanchez-Pinto and Bennett contributed equally to this manuscript; Dr. DeWitt and Mr. Russell contributed equally to the manuscript), "International Consensus Criteria for Pediatric Sepsis and Septic Shock" by Schlapbach, Watson, Sorce, Argent, et al. (2024) <doi:10.1001/jama.2024.0179> (Drs Schlapbach, Watson, Sorce, and Argent contributed equally) and the application note "phoenix: an R package and Python module for calculating the Phoenix pediatric sepsis score and criteria" by DeWitt, Russell, Rebull, Sanchez-Pinto, and Bennett (2024) <doi:10.1093/jamiaopen/ooae066>.

Power logit regression models for bounded continuous data, in which the density generator may be normal, Student-t, power exponential, slash, hyperbolic, sinh-normal, or type II logistic. Diagnostic tools associated with the fitted model, such as the residuals, local influence measures, leverage measures, and goodness-of-fit statistics, are implemented. The estimation process follows the maximum likelihood approach and, currently, the package supports two types of estimators: the usual maximum likelihood estimator and the penalized maximum likelihood estimator. More details about power logit regression models are described in Queiroz and Ferrari (2022) <arXiv:2202.01697>.

Estimate sample size based on precision rather than power. precisely is a study planning tool to calculate sample size based on precision. Power calculations are focused on whether or not an estimate will be statistically significant; calculations of precision are based on the same principles as power calculation but turn the focus to the width of the confidence interval. precisely is based on the work of Rothman and Greenland (2018).

This package creates a non-negative low-rank approximate factorization of a sparse counts matrix by maximizing Poisson likelihood with L1/L2 regularization (e.g. for implicit-feedback recommender systems or bag-of-words-based topic modeling) (Cortes, (2018) <arXiv:1811.01908>), which usually leads to very sparse user and item factors (over 90% zero-valued). Similar to hierarchical Poisson factorization (HPF), but follows an optimization-based approach with regularization instead of a hierarchical prior, and is fit through gradient-based methods instead of variational inference.

This package provides tools for Natural Language Processing in French and texts from Marcel Proust's collection "A La Recherche Du Temps Perdu". The novels contained in this collection are "Du cote de chez Swann ", "A l'ombre des jeunes filles en fleurs","Le Cote de Guermantes", "Sodome et Gomorrhe I et II", "La Prisonniere", "Albertine disparue", and "Le Temps retrouve".

Conduct power analyses and inference of marginal effects. Uses plug-in estimation and influence functions to perform robust inference, optionally leveraging historical data to increase precision with prognostic covariate adjustment. The methods are described in Højbjerre-Frandsen et al. (2025) <doi:10.48550/arXiv.2503.22284>.

This package implements the methodology of Huling, Smith, and Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part modeling framework to handle semi-continuous data by separately modeling the positive part of the outcome and an indicator of whether each outcome is positive, but still results in a single treatment rule. High dimensional data is handled with a cooperative lasso penalty, which encourages the coefficients in the two models to have the same sign.

Identifies potential target sequences for a given set of primers and generates phylogenetic trees annotated with the taxonomies of the predicted amplification products.

Helper functions for package creation, building and maintenance. Designed to work with a build system such as GNU make or package fakemake to help you to conditionally work through the stages of package development (such as spell checking, linting, testing, before building and checking a package).

Defines functions to describe regression models using only pre-computed summary statistics (i.e. means, variances, and covariances) in place of individual participant data. Possible models include linear models for linear combinations, products, and logical combinations of phenotypes. Implements methods presented in Wolf et al. (2021) <doi:10.3389/fgene.2021.745901> Wolf et al. (2020) <doi:10.1142/9789811215636_0063> and Gasdaska et al. (2019) <doi:10.1142/9789813279827_0036>.

This package provides a set of raw datasets used to create SDTM domains in pharmaversesdtm package.

Useful set of tools for plotting network diagrams in any kind of project.

Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations allow for transforming variables with distributions distant from 0 to normality. Transformers are implemented as S4 objects. These allow for transforming new instances to normality after optimising fitting parameters on other data. A test for central normality allows for rejecting transformations that fail to produce a suitably normal distribution, independent of sample number.

This package implements the Single Transferable Vote (STV) electoral system, with clear explanatory graphics. The core function stv() uses Meek's method, the purest expression of the simple principles of STV, but which requires electronic counting. It can handle votes expressing equal preferences for subsets of the candidates. A function stv.wig() implementing the Weighted Inclusive Gregory method, as used in Scottish council elections, is also provided, and with the same options, as described in the manual. The required vote data format is as an R list: a function pref.data() is provided to transform some commonly used data formats into this format. References for methodology: Hill, Wichmann and Woodall (1987) <doi:10.1093/comjnl/30.3.277>, Hill, David (2006) <https://www.votingmatters.org.uk/ISSUE22/I22P2.pdf>, Mollison, Denis (2023) <arXiv:2303.15310>, (see also the package manual pref_pkg_manual.pdf).

This package implements classical and contemporary utility-analysis methods for personnel selection, organised by criterion scale (classification or continuous/monetary) and selection structure (compensatory or multiple-hurdle). Methods include Taylor-Russell classification (Taylor and Russell, 1939, <doi:10.1037/h0057079>), Brogden-Cronbach-Gleser monetary utility (Brogden, 1949, <doi:10.1111/j.1744-6570.1949.tb01397.x>), Schmidt-Hunter-Pearlman intervention utility (Schmidt and others, 1979, <doi:10.1037/0021-9010.64.6.609>), Sturman comprehensive cascade (Sturman, 2001, <doi:10.1108/eb029072>), Thomas-Owen-Gunst multivariate classification (Thomas and others, 1977, <doi:10.3102/10769986002001055>), compensatory versus multiple-hurdle simulation (Ock and Oswald, 2018, <doi:10.1027/1866-5888/a000205>), AUC-to-effect-size conversions (Salgado, 2018, <doi:10.5093/ejpalc2018a5>), Pareto frontiers for validity-diversity trade-offs, and Monte Carlo uncertainty propagation.

Create a project directory structure, along with typical files for that project. This allows projects to be quickly and easily created, as well as for them to be standardized. Designed specifically with scientists in mind (mainly bio-medical researchers, but likely applies to other fields).

We provide several algorithms to compute the genotype ancestry scores (such as eigenvector projections) in the case where highly correlated individuals are involved.

Exports an enhanced version of the tools::parseLatex() function to handle LaTeX syntax more accurately. Also includes numerous functions for searching and modifying LaTeX source.

This package provides functions for easily reading and processing binary data files created by Pamguard (<https://www.pamguard.org/>). All functions for directly reading the binary data files are based on MATLAB code written by Michael Oswald.

An implementation of the one-step privacy-protecting method for estimating the overall and site-specific hazard ratios using inverse probability weighted Cox models in distributed data network studies, as proposed by Shu, Yoshida, Fireman, and Toh (2019) <doi: 10.1177/0962280219869742>. This method only requires sharing of summary-level riskset tables instead of individual-level data. Both the conventional inverse probability weights and the stabilized weights are implemented.

Find recursive dependencies of R packages from various sources. Solve the dependencies to obtain a consistent set of packages to install. Download packages, and install them. It supports packages on CRAN', Bioconductor and other CRAN-like repositories, GitHub', package URLs', and local package trees and files. It caches metadata and package files via the pkgcache package, and performs all HTTP requests, downloads, builds and installations in parallel. pkgdepends is the workhorse of the pak package.

This package provides functions for fitting and validation of models for subgroup identification and personalized medicine / precision medicine under the general subgroup identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>. This package is intended for use for both randomized controlled trials and observational studies and is described in detail in Huling and Yu (2021) <doi:10.18637/jss.v098.i05>.

Streamline the management, creation, and formatting of panel data from the Panel Study of Income Dynamics ('PSID') <https://psidonline.isr.umich.edu> using this user-friendly tool. Simply define variable names and input code book details directly from the PSID official website, and this toolbox will efficiently facilitate the data preparation process, transforming raw PSID files into a well-organized format ready for further analysis.