This package implements an algorithm for generating maps, known as tile maps, in which each region is represented by a single tile of the same shape and size. The algorithm was first proposed in "Generating Tile Maps" by Graham McNeill and Scott Hale (2017) <doi:10.1111/cgf.13200>. Functions allow users to generate, plot, and compare square or hexagon tile maps.

This package provides Apache Spark style window aggregation for R dataframes and remote dbplyr tables via mutate in dplyr flavour.

Base R sometimes requires verbose statements for simple, often recurring tasks, such as printing text without trailing space, ending with newline. This package aims at providing shorthands for such tasks.

Collection of functions that allow to export data frames to excel workbook.

Package designed for working with vectors and lists of vectors, mainly for turning them into other indexed data structures.

This package implements the TabNet model by Sercan O. Arik et al. (2019) <doi:10.48550/arXiv.1908.07442> with Coherent Hierarchical Multi-label Classification Networks by Giunchiglia et al. <doi:10.48550/arXiv.2010.10151> and provides a consistent interface for fitting and creating predictions. It's also fully compatible with the tidymodels ecosystem.

The goal of tosr is to create the Tree of Science from Web of Science (WoS) and Scopus data. It can read files from both sources at the same time. More information can be found in Valencia-Hernández (2020) <https://revistas.unal.edu.co/index.php/ingeinv/article/view/77718>.

This package provides tools to calculate trait probability density functions (TPD) at any scale (e.g. populations, species, communities). TPD functions are used to compute several indices of functional diversity, as well as its partition across scales. These indices constitute a unified framework that incorporates the underlying probabilistic nature of trait distributions into uni- or multidimensional functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for further information.

This package provides a shiny app that generates plots and summary tables from repeat-dose toxicology study results to facilitate holistic evaluation of the drug safety of active pharmaceutical ingredients (API) prior to initiation of clinical trials.

Univariate time series operations that follow an opinionated design. The main principle of transx is to keep the number of observations the same. Operations that reduce this number have to fill the observations gap.

This package provides an easy-to-use tind class representing time indices of different types (years, quarters, months, ISO 8601 weeks, dates, time of day, date-time, and arbitrary integer/numeric indices). Includes an extensive collection of functions for calendrical computations (including business applications), index conversions, index parsing, and other operations. Auxiliary classes representing time differences and time intervals (with set operations and index matching functionality) are also provided. All routines have been optimised for speed in order to facilitate computations on large datasets. More details regarding calendars in general and calendrical algorithms can be found in "Calendar FAQ" by Claus Tøndering <https://www.tondering.dk/claus/calendar.html>.

This package provides methods for handling the missing values outliers are introduced in this package. The recognized missing values and outliers are replaced using a model-based approach. The model may consist of both autoregressive components and external regressors. The methods work robust and efficient, and they are fully tunable. The primary motivation for writing the package was preprocessing of the energy systems data, e.g. power plant production time series, but the package could be used with any time series data. For details, see Narajewski et al. (2021) <doi:10.1016/j.softx.2021.100809>.

Enables all rstan functionality for a TMB model object, in particular MCMC sampling and chain visualization. Sampling can be performed with or without Laplace approximation for the random effects. This is demonstrated in Monnahan & Kristensen (2018) <DOI:10.1371/journal.pone.0197954>.

This package performs maximum likelihood based estimation and inference on time to event data, possibly subject to non-informative right censoring. FitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. CompParaSurv() compares the mean, median, and restricted mean survival experiences of two treatment groups. Candidate distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull.

The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Paper: Albrecht, Marco, et al. (2017)<DOI:10.1186/s12859-016-1440-8>.

This package implements the TWO-Component Single Cell Model-Based Association Method (TWO-SIGMA) for gene-level differential expression (DE) analysis and DE-based gene set testing of single-cell RNA-sequencing datasets. See Van Buren et al. (2020) <doi:10.1002/gepi.22361> and Van Buren et al. (2021) <doi:10.1101/2021.01.24.427979>.

To handle higher-order tensor data. See Kolda and Bader (2009) <doi:10.1137/07070111X> for details on tensor. While existing packages on tensor data extend the base array class to some data classes, this package serves as an alternative resort to handle tensor only as array class. Some functionalities related to missingness are also supported.

Helps the user to build and register schema descriptions of disorganised (messy) tables. Disorganised tables are tables that are not in a topologically coherent form, where packages such as tidyr could be used for reshaping. The schema description documents the arrangement of input tables and is used to reshape them into a standardised (tidy) output format.

Pre-process for discrete time series data set which is not continuous at the column of date'. Refilling records of missing date and other columns to the hollow data set so that final data set is able to be dealt with time series analysis.

You only need to type why pie charts are bad on Google to find thousands of articles full of (valid) reasons why other types of charts should be preferred over this one. Therefore, because of the little use due to the reasons already mentioned, making pie charts (and related) in R is not straightforward, so other functions are needed to simplify things. In this R package there are useful functions to make tasty pie charts immediately by exploiting the many cool templates provided.

Utilizing the logger framework to record events within a package, specific to teal family of packages. Supports logging namespaces, hierarchical logging, various log destinations, vectorization, and more.

Generic methods for parameter tuning of classification algorithms using multiple scoring functions (Muessel et al. (2012), <doi:10.18637/jss.v046.i05>).

This package provides tools for the exploration of distributions of phylogenetic trees. This package includes a shiny interface which can be started from R using treespaceServer(). For further details see Jombart et al. (2017) <DOI:10.1111/1755-0998.12676>.

This package performs Thresholded Ordered Sparse Canonical Correlation Analysis (CCA). For more details see Senar, N. (2024) <doi:10.1093/bioadv/vbae021> and Senar, N. et al. (2025) <doi:10.48550/arXiv.2503.15140>.