Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions.

This package provides tools for constructing longitudinal decision paths, quantifying temporal drift, tracking subgroup disparity trajectories, and stress-testing longitudinal conclusions under hidden bias. Implements three signature metrics: the Drift Intensity Index (DII), which measures structural instability in transition dynamics using the Frobenius norm of consecutive transition matrix differences; the Bias Amplification Index (BAI), which quantifies whether group disparities widen or converge over time; and the Temporal Fragility Index (TFI), which estimates the minimum hidden-bias perturbation required to nullify a longitudinal trend conclusion. An interactive shiny application supports exploratory analysis, visualization, and reproducible reporting. Methods are motivated by applications in educational and social science research, including the Early Childhood Longitudinal Study (ECLS). The DII is based on the Frobenius norm as described in Golub and Van Loan (2013, ISBN:9781421407944). The TFI extends the hidden-bias sensitivity framework of Rosenbaum (2002, ISBN:9781441912633). The BAI draws on disparity-trajectory methods discussed in Duncan and Murnane (2011, ISBN:9780871542731).

Wrapper for Datamuse API to find rhyming and other associated words. This includes words of similar meaning, spelling, or other related words. Learn more about the Datamuse API here <https://www.datamuse.com/api/>.

This package provides a client for the public API of data.gouv.fr, the French government's open data platform. It helps you find a dataset that matches your interests, judge whether it is usable, download it, and re-fetch the exact same table later in a reproducible way. You can search the catalog and filter by producer or theme (dg_find_datasets(), dg_find_organization(), dg_find_topics()), pull a dataset's tabular resources into tidy tibbles (dg_pull_dataset()), inspect the documented variables of its data schema (dg_schema()), and compute summary metrics such as size, number of columns and missing-value rate (dg_summary(), dg_summarise()). Each returned table carries a stable identifier (dg_table_id(), dg_refetch()) so it can be re-fetched later. Requests are built on top of httr2'.

This package provides a shiny module to facilitate page layouts with resizable panes for page content based on split.js JavaScript library (<https://split.js.org>).

This package implements an interface to Minecraft (Bedrock Edition) worlds. Supports the analysis and management of these worlds and game saves.

Designed to streamline data analysis and statistical testing, reducing the length of R scripts while generating well-formatted outputs in pdf', Microsoft Word', and Microsoft Excel formats. In essence, the package contains functions which are sophisticated wrappers around existing R functions that are called by using f_ (user f_riendly) prefix followed by the normal function name. This third version of the rfriend package focuses primarily on data exploration, including tools for creating summary tables, f_summary(), summary figures, f_scan(), outlier detection and removal, f_outlier() and f_remove_outliers(), performing data transformations, f_boxcox() in part based on MASS/boxcox and rcompanion', and f_bestNormalize() which wraps and extends functionality from the bestNormalize package. Furthermore, rfriend can automatically (or on request) generate visualizations such as boxplots, f_boxplot(), QQ-plots, f_qqnorm(), and histograms f_hist(). Additionally, the package includes several statistical test functions: f_aov(), f_chisq_test(), f_corplot(), f_kruskal_test(), f_lmer(), f_glm(), f_t_test(), f_wilcox_test(), for sequential testing and visualisation of the similar named stats functions. These functions, except for f_chisq_test(), support testing multiple response variables and predictors, while also handling assumption checks, data transformations, and post hoc tests. Post hoc results are automatically summarized in a table using the compact letter display (cld) format for easy interpretation. The package also provides a function to do model comparison, f_model_comparison(), and several utility functions to simplify common R tasks. For example, f_clear() clears the workspace and restarts R with a single command; f_setwd() sets the working directory to match the directory of the current script; f_theme() quickly changes RStudio themes; and f_factors() converts multiple columns of a data frame to factors, and much more. If you encounter any issues or have feature requests, please feel free to contact me via email.

This package provides functions for dissimilarity analysis and machine learning in complex spectral data sets, including memory-based learning (MBL), optimal subset search and selection, and retrieval-based modelling with model libraries. Supports local learning, optimisation of spectral libraries, and ensemble prediction from precomputed models. Most of these functions are based on the methods presented in Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>, Ramirez-Lopez et al. (2026a) <doi:10.1016/j.aca.2026.345682>, and Ramirez-Lopez et al. (2026b) <doi:10.1016/j.aca.2026.345651>.

FRACTRAN is an obscure yet tantalizing programming language invented by John Conway of Game of Life fame. The code consists of a sequence of fractions. The rules are simple. First, select an integer to initialize the process. Second, multiply the integer by the first fraction. If an integer results, start again with the new integer. If not, try the next fraction. Finally, if no such multiplication yields an integer, terminate the program. For more information, see <https://en.wikipedia.org/wiki/FRACTRAN> .

Defines functions that can be used to collect provenance as an R script executes or during a console session. The output is a text file in PROV-JSON format.

The basic algorithm to perform the folding test of unimodality. Given a dataset X (d dimensional, n samples), the test checks whether the distribution of the data are rather unimodal or rather multimodal. This package stems from the following research publication: Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. "Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018. <doi:10.1145/3219819.3219994>.

This package provides methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series. General description of the methods with many examples can be found in the book Golyandina (2018, <doi:10.1007/978-3-662-57380-8>). See citation("Rssa") for details.

Fit the reduced-rank multinomial logistic regression model for Markov chains developed by Wang, Abner, Fardo, Schmitt, Jicha, Eldik and Kryscio (2021)<doi:10.1002/sim.8923> in R. It combines the ideas of multinomial logistic regression in Markov chains and reduced-rank. It is very useful in a study where multi-states model is assumed and each transition among the states is controlled by a series of covariates. The key advantage is to reduce the number of parameters to be estimated. The final coefficients for all the covariates and the p-values for the interested covariates will be reported. The p-values for the whole coefficient matrix can be calculated by two bootstrap methods.

This package implements simple Hamiltonian Monte Carlo routines in R for sampling from any desired target distribution which is continuous and smooth. See Neal (2017) <arXiv:1701.02434> for further details on Hamiltonian Monte Carlo. Automatic parameter selection is not supported.

This package provides a rolling version of the Latent Dirichlet Allocation, see Rieger et al. (2021) <doi:10.18653/v1/2021.findings-emnlp.201>. By a sequential approach, it enables the construction of LDA-based time series of topics that are consistent with previous states of LDA models. After an initial modeling, updates can be computed efficiently, allowing for real-time monitoring and detection of events or structural breaks.

This package provides tools to derive species-level phylogenies from large synthesis mega-trees for a wide range of taxonomic groups, including plants, birds, mammals, amphibians, reptiles, fish, bees, butterflies, and sharks. When a queried species is absent from the mega-tree, it is grafted onto the tree using one of two placement strategies: attachment at the basal node of the most closely related genus or family ('at_basal_node'), or random attachment below that basal node with probability proportional to branch length ('random_below_basal'). See Li (2023) <doi:10.1111/ecog.06643> for details. Multiple species from a genus not represented in the mega-tree are placed as a polytomy to preserve clade coherence. The package interfaces with the megatrees data package, which bundles or downloads on demand curated mega-trees. Users can also provide their own mega-trees.

This package provides an easy way to report the results of ROC analysis, including: 1. an ROC curve. 2. the value of Cutoff, AUC (Area Under Curve), ACC (accuracy), SEN (sensitivity), SPE (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), PPV (positive predictive value), NPV (negative predictive value), PPA (percentage of positive accordance), NPA (percentage of negative accordance), TPA (percentage of total accordance), KAPPA (kappa value).

Offers a handful of useful wrapper functions which streamline the reading, analyzing, and visualizing of variant call format (vcf) files in R. This package was designed to facilitate an explicit pipeline for optimizing Stacks (Rochette et al., 2019) (<doi:10.1111/mec.15253>) parameters during de novo (without a reference genome) assembly and variant calling of restriction-enzyme associated DNA sequence (RADseq) data. The pipeline implemented here is based on the 2017 paper "Lost in Parameter Space" (Paris et al., 2017) (<doi:10.1111/2041-210X.12775>) which establishes clear recommendations for optimizing the parameters m', M', and n', during the process of assembling loci.

This package provides a piped query generator based on Edgar F. Codd's relational algebra, and on production experience using SQL and dplyr at big data scale. The design represents an attempt to make SQL more teachable by denoting composition by a sequential pipeline notation instead of nested queries or functions. The implementation delivers reliable high performance data processing on large data systems such as Spark', databases, and data.table'. Package features include: data processing trees or pipelines as observable objects (able to report both columns produced and columns used), optimized SQL generation as an explicit user visible table modeling step, plus explicit query reasoning and checking.

The Echo nest <http://the.echonest.com> is the industry's leading music intelligence company, providing developer with deepest understanding of music content and music fans. This package can be used to access artist's data including songs, blogs, news, reviews etc. Song's data including audio summary, style, danceability, tempo etc can also be accessed.

This package contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. Frost, H. Robert (2023) "Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error" <doi:10.1101/2023.04.03.535366>.

Connection to the Redis (or Valkey') key/value store using the C-language client library hiredis (included as a fallback) with MsgPack encoding provided via RcppMsgPack headers. It now also includes the pub/sub functions from the rredis package.

This package provides plotting helpers, table-formatting utilities, and report templates for econometrics, model development, and applied data analysis. Includes University of Cincinnati branded themes for ggplot2', modelsummary', flextable', rmarkdown', bookdown', and quarto'.

The ability to plot raster graphics in PDF files can be useful when one needs multi-page documents, but the plots contain so many individual elements that (the usual) use of vector graphics results in inconveniently large file sizes. Internally, the package plots each individual page as a PNG, and then combines them in one PDF file.