An elegant tool for processing and visualizing lipidomics data generated by mass spectrometry. LipidomicsR simplifies channel and replicate handling while providing thorough lipid species annotation. Its visualization capabilities encompass principal components analysis plots, heatmaps, volcano plots, and radar plots, enabling concise data summarization and quality assessment. Additionally, it can generate bar plots and line plots to visualize the abundance of each lipid species.

This package provides a lightweight interchange layer for single-cell and spatial omics data, built on the L-star model of labelled axes and typed fields over them, serialized to the Zarr format. Provides bidirectional converters ("profiles") for Seurat', SingleCellExperiment', Conos', and pagoda2 objects, including collections of heterogeneous samples, via a shared C++ core ('libstar') so the same store is readable from R, Python', and C++.

This package implements bootstrap methods for linear regression models with errors following a time-varying process, focusing on approximating the distribution of the least-squares estimator for regression models with locally stationary errors. It enables the construction of bootstrap and classical confidence intervals for regression coefficients, leveraging intensive simulation studies and real data analysis.

This package provides functions for summarizing, visualizing, and analyzing Likert-scale survey data. Includes support for computing descriptive statistics, Relative Importance Index (RII), reliability analysis (Cronbach's Alpha), and response distribution plots.

Time series analysis based on lambda transformer and variational seq2seq, built on Torch'.

Assign meaningful labels to data frame columns. labelmachine manages your label assignment rules in yaml files and makes it easy to use the same labels in multiple projects.

Alternate font rendering is useful when rendering text to novel graphics outputs where modern font rendering is not available or where bespoke text positioning is required. Bitmap and vector fonts allow for custom layout and rendering using pixel coordinates and line drawing. Formatted text is created as a data.frame of pixel coordinates (for bitmap fonts) or stroke coordinates (for vector fonts). All text can be easily previewed as a matrix or raster image. A selection of fonts is included with this package.

Here we provide an implementation of the linear and logistic regression-based Reliable Change Index (RCI), to be used with lm and binomial glm model objects, respectively, following Moral et al. <https://psyarxiv.com/gq7az/>. The RCI function returns a score assumed to be approximately normally distributed, which is helpful to detect patients that may present cognitive decline.

This package provides a set of streamlined functions that allow easy generation of linear regression diagnostic plots necessarily for checking linear model assumptions. This package is meant for easy scheming of linear regression diagnostics, while preserving merits of "The Grammar of Graphics" as implemented in ggplot2'. See the ggplot2 website for more information regarding the specific capability of graphics.

This package implements leave-out estimation of variance components in two-way fixed effects models as an R translation of the original MATLAB package of Kline, Saggio, and Solvsten (2020) <doi:10.3982/ECTA16410>. The package includes graph-based connected-set pruning, leave-out bias correction, leverage computation by exact and randomized algorithms, fixed effect estimation helpers, and companion model-fit summaries for matched worker-firm panels in the spirit of Abowd, Kramarz, and Margolis (1999) <doi:10.1111/1468-0262.00020>.

Repairs malformed JSON strings, particularly those generated by Large Language Models. Handles missing quotes, trailing commas, unquoted keys, and other common JSON syntax errors.

Estimate drift and diffusion functions from time series and generate synthetic time series from given drift and diffusion coefficients.

This package provides a collection of helper functions and illustrative datasets to support learning and teaching of data science with R. The package is designed as a companion to the book <https://book-data-science-r.netlify.app>, making key data science techniques accessible to individuals with minimal coding experience. Functions include tools for data partitioning, performance evaluation, and data transformations (e.g., z-score and min-max scaling). The included datasets are curated to highlight practical applications in data exploration, modeling, and multivariate analysis. An early inspiration for the package came from an ancient Persian idiom about "eating the liver", symbolizing deep and immersive engagement with knowledge.

This package provides a shiny application to construct age-specific life tables and fertility schedules from individual female daily egg records. The application computes age-specific survival and fertility functions and estimates key demographic parameters including the net reproductive rate, mean generation time, intrinsic rate of increase, finite rate of increase and doubling time. Optional confidence intervals can be obtained using percentile bootstrap or delete-1 jackknife resampling at the female level. Methods and definitions follow Stevens (2009) <doi:10.1007/978-0-387-89882-7> and Rossini et al. (2024) <doi:10.1371/journal.pone.0299598>.

An implementation of list comprehensions as purely syntactic sugar with a minor runtime overhead. It constructs nested for-loops and executes the byte-compiled loops to collect the results.

Effectively simulates the discretization process inherent to Likert scales while minimizing distortion. It converts continuous latent variables into ordinal categories to generate Likert scale item responses. Particularly useful for accurately modeling and analyzing survey data that use Likert scales, especially when applying statistical techniques that require metric data.

An easy tool to transform 2D longitudinal data into 3D arrays suitable for Long short-term memory neural networks training. The array output can be used by the keras package. Long short-term memory neural networks are described in: Hochreiter, S., & Schmidhuber, J. (1997) <doi:10.1162/neco.1997.9.8.1735>.

Lipid annotation in untargeted LC-MS lipidomics based on fragmentation rules. Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) <doi:10.1021/acs.analchem.8b03409>.

Navigating the shift of clinical laboratory data from primary everyday clinical use to secondary research purposes presents a significant challenge. Given the substantial time and expertise required for lab data pre-processing and cleaning and the lack of all-in-one tools tailored for this need, we developed our algorithm lab2clean as an open-source R-package. lab2clean package is set to automate and standardize the intricate process of cleaning clinical laboratory results. With a keen focus on improving the data quality of laboratory result values and units, our goal is to equip researchers with a straightforward, plug-and-play tool, making it smoother for them to unlock the true potential of clinical laboratory data in clinical research and clinical machine learning (ML) model development. Functions to clean & validate result values (Version 1.0) are described in detail in Zayed et al. (2024) <doi:10.1186/s12911-024-02652-7>. Functions to standardize & harmonize result units (added in Version 2.0) are described in detail in Zayed et al. (2025) <doi:10.1016/j.ijmedinf.2025.106131>.

This package provides tools for fast and accurate evaluation of skew stable distributions (CDF, PDF and quantile functions), random number generation, and parameter estimation. This is libstableR as per Royuela del Val, Simmross-Wattenberg, and Alberola López (2017) <doi:10.18637/jss.v078.i01> under a new maintainer.

This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model.

This package provides a set of all-cause and cause-specific life expectancy sensitivity and decomposition methods, including Arriaga (1984) <doi:10.2307/2061029>, others documented by Ponnapalli (2005) <doi:10.4054/DemRes.2005.12.7>, lifetable, numerical, and other analytic or algorithmic approaches such as Horiuchi et al (2008) <doi:10.1353/dem.0.0033>, or Andreev et al (2002) <doi:10.4054/DemRes.2002.7.14>.

The programs were developed for estimation of parameters and testing exponential versus Pareto distribution during our work on hydrologic extremes. See Kozubowski, T.J., A.K. Panorska, F. Qeadan, and A. Gershunov (2007) <doi:10.1080/03610910802439121>, and Panorska, A.K., A. Gershunov, and T.J. Kozubowski (2007) <doi:10.1007/978-0-387-34918-3_26>.

This package provides tools for fitting linear mixed models using sparse matrix methods and variance component estimation. Applications include spline-based modeling of spatial and temporal trends using penalized splines (Boer, 2023) <doi:10.1177/1471082X231178591>.