Key-value store, implemented as a wrapper around LMDB'; the "lightning memory-mapped database" <https://www.symas.com/mdb>. LMDB is a transactional key value store that uses a memory map for efficient access. This package wraps the entire LMDB interface (except duplicated keys), and provides objects for transactions and cursors.

Calculates total survey error (TSE) for one or more surveys, using both scale-dependent and scale-independent metrics. Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTIND and its documentation), properly input column names (see functions documentation), and run your functions. For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.

Provide data generation and estimation tools for the truncated positive normal (tpn) model discussed in Gomez, Olmos, Varela and Bolfarine (2018) <doi:10.1007/s11766-018-3354-x>, the slash tpn distribution discussed in Gomez, Gallardo and Santoro (2021) <doi:10.3390/sym13112164>, the bimodal tpn distribution discussed in Gomez et al. (2022) <doi:10.3390/sym14040665>, the flexible tpn model <doi:10.3390/math11214431> and the unit tpn distribution <doi:10.1016/j.chemolab.2025.105322>.

This package provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.

This is a tidy implementation for heatmap. At the moment it is based on the (great) package ComplexHeatmap'. The goal of this package is to interface a tidy data frame with this powerful tool. Some of the advantages are: Row and/or columns colour annotations are easy to integrate just specifying one parameter (column names). Custom grouping of rows is easy to specify providing a grouped tbl. For example: df %>% group_by(...). Labels size adjusted by row and column total number. Default use of Brewer and Viridis palettes.

Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) <doi:10.1016/j.foodqual.2015.06.017>, Castura, Baker, and Ross (2016) <doi:10.1016/j.foodqual.2016.06.011>, and Pineau et al. (2009) <doi:10.1016/j.foodqual.2009.04.005>.

Allows users to quickly load multiple patients electrocardiographic (ECG) data at once and conduct relevant time analysis of heart rate variability (HRV) without manual edits from a physician or data cleaning specialist. The package provides the unique ability to iteratively filter, plot, and store time analysis results in a data frame while writing plots to a predefined folder. This streamlines the workflow for HRV analysis across multiple datasets. Methods are based on Rodrà guez-Liñares et al. (2011) <doi:10.1016/j.cmpb.2010.05.012>. Examples of applications using this package include Kwon et al. (2022) <doi:10.1007/s10286-022-00865-2> and Lawrence et al. (2023) <doi:10.1016/j.autneu.2022.103056>.

This package provides a unified tidyverse-compatible interface to R's machine learning packages. Wraps established implementations from glmnet', randomForest', xgboost', e1071', rpart', gbm', nnet', cluster', dbscan', and others - providing consistent function signatures, tidy tibble output, and unified ggplot2'-based visualization. The underlying algorithms are unchanged; tidylearn simply makes them easier to use together. Access raw model objects via the $fit slot for package-specific functionality. Methods include random forests Breiman (2001) <doi:10.1023/A:1010933404324>, LASSO regression Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, elastic net Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, support vector machines Cortes and Vapnik (1995) <doi:10.1007/BF00994018>, and gradient boosting Friedman (2001) <doi:10.1214/aos/1013203451>.

Calculate Expert Team on Climate Change Detection and Indices (ETCCDI) <-- (acronym) climate indices from daily or hourly temperature and precipitation data. Provides flexible data handling.

Two-stage procedure compares hazard rate functions, which may or may not cross each other.

Computes how the correlation between 2 time-series changes over time. To do so, the package follows the method from Choi & Shin (2021) <doi:10.1007/s42952-020-00073-6>. It performs a non-parametric kernel smoothing (using a common bandwidth) of all underlying components required for the computation of a correlation coefficient (i.e., x, y, x^2, y^2, xy). An automatic selection procedure for the bandwidth parameter is implemented. Alternative kernels can be used (Epanechnikov, box and normal). Both Pearson and Spearman correlation coefficients can be estimated and change in correlation over time can be tested.

The modern database TileDB introduces a powerful on-disk format for storing and accessing any complex data based on multi-dimensional arrays. It supports dense and sparse arrays, dataframes and key-values stores, cloud storage ('S3', GCS', Azure'), chunked arrays, multiple compression, encryption and checksum filters, uses a fully multi-threaded implementation, supports parallel I/O, data versioning ('time travel'), metadata and groups. It is implemented as an embeddable cross-platform C++ library with APIs from several languages, and integrations. This package provides the R support.

Automates documentation of test_that() calls within R test files. The package scans test sources, extracts human-readable test titles (even when composed with functions like paste() or glue::glue(), ... etc.), and generates reproducible roxygen2-style listings that can be inserted both globally and per-section. It ensures idempotent updates and supports customizable numbering templates with hierarchical indices. Designed for developers, QA teams, and package maintainers seeking consistent, self-documenting test inventories.

Multinomial (inverse) regression inference for text documents and associated attributes. For details see: Taddy (2013 JASA) Multinomial Inverse Regression for Text Analysis <arXiv:1012.2098> and Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>. A minimalist partial least squares routine is also included. Note that the topic modeling capability of earlier textir is now a separate package, maptpx'.

Download and compile any version of the IANA Time Zone Database (also known as Olson database) and make it current in your R session. Beware: on Windows Cygwin is required!

Simulation of random vectors from truncated multivariate normal and t distributions based on the algorithms proposed by Yifang Li and Sujit K. Ghosh (2015) <doi:10.1080/15598608.2014.996690>.

This package implements the truncated harmonic mean estimator (THAMES) of the reciprocal marginal likelihood for uni- and multivariate mixture models using posterior samples and unnormalized log posterior values via reciprocal importance sampling. Metodiev, Irons, Perrot-Dockès, Latouche & Raftery (2025) <doi:10.48550/arXiv.2504.21812>.

This package implements tipping point sensitivity analysis for time-to-event endpoints under different missing data scenarios, as described in Oodally et al. (2025) <doi:10.48550/arXiv.2506.19988>. Supports both model-based and model-free imputation, multiple imputation workflows, plausibility assessment and visualizations. Enables robust assessment for regulatory and exploratory analyses.

Read General Transit Feed Specification (GTFS) zipfiles into a list of R dataframes. Perform validation of the data structure against the specification. Analyze the headways and frequencies at routes and stops. Create maps and perform spatial analysis on the routes and stops. Please see the GTFS documentation here for more detail: <https://gtfs.org/>.

This package implements the truncated harmonic mean estimator (THAMES) of the reciprocal marginal likelihood using posterior samples and unnormalized log posterior values via reciprocal importance sampling. Metodiev, Perrot-Dockès, Ouadah, Irons, Latouche, & Raftery (2024). Bayesian Analysis. <doi:10.1214/24-BA1422>.

Bootstrapped response and correlation functions, seasonal correlations and evaluation of reconstruction skills for use in dendroclimatology and dendroecology, see Zang and Biondi (2015) <doi:10.1111/ecog.01335>.

Fitting tree-structured varying coefficient models (Berger et al. (2019), <doi:10.1007/s11222-018-9804-8>). Simultaneous detection of covariates with varying coefficients and effect modifiers that induce varying coefficients if they are present.

Evaluation of alternatives based on multiple criteria using TOPSIS method.

This package implements a task queue system for asynchronous parallel computing using PostgreSQL <https://www.postgresql.org/> as a backend. Designed for embarrassingly parallel problems where tasks do not communicate with each other. Dynamically distributes tasks to workers, handles uneven load balancing, and allows new workers to join at any time. Particularly useful for running large numbers of independent tasks on high-performance computing (HPC) clusters with SLURM <https://slurm.schedmd.com/> job schedulers.