We present a range of simulations to aid researchers in determining appropriate sample sizes when performing critical thermal limits studies (e.g. CTmin/CTmin experiments). A number of wrapper functions are provided for plotting and summarising outputs from these simulations. This package is presented in van Steenderen, C.J.M., Sutton, G.F., Owen, C.A., Martin, G.D., and Coetzee, J.A. Sample size assessments for thermal physiology studies: An R package and R Shiny application. 2023. Physiological Entomology. <doi:10.1111/phen.12416>. The GUI version of this package is available on the R Shiny online server at: <https://clarkevansteenderen.shinyapps.io/ThermalSampleR_Shiny/> , or it is accessible via GitHub at <https://github.com/clarkevansteenderen/ThermalSampleR_Shiny/>. We would like to thank Grant Duffy (University of Otago, Dundedin, New Zealand) for granting us permission to use the source code for the Test of Total Equivalency function.

This package implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial and cross dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes.

Includes functions for mapping named lists to function arguments, random strings, pasting and combining rows together across columns, etc.

Interactive shiny application for working with textmining and text analytics. Various visualizations are provided.

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

This groundbreaking technical indicator directly integrates volatility into price averaging by weighting median range-bound prices using the True Range. Unlike conventional metrics such as TWAP (Time-Weighted Average Price), which focuses solely on time, or VWAP (Volume-Weighted Average Price), which emphasizes volume, TrueWAP captures fluctuating market behavior by reflecting true price movement within high/low performance boundaries.

This package provides a shared tsibble data easily communicates between htmlwidgets on both client and server sides, powered by crosstalk'. A shiny module is provided to visually explore periodic/aperiodic temporal patterns.

This package provides a unified interface to extract trends, cycles, and seasonal components from monthly and quarterly time series using established econometric filters and smoothing methods, with frequency-aware defaults for common economic frequencies.

Measuring tree architecture from terrestrial lidar data, including tree-level properties, crown characteristics, and structural attributes derived from quantitative structure models (QSMs).

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.

This package implements two-mode clustering (biclustering) using genetic algorithms. The method was first introduced in Hageman et al. (2008) <doi:10.1007/s11306-008-0105-7>. The package provides tools for fitting, visualization, and validation of two-mode cluster structures in data matrices.

This package provides twin support vector machine classifiers and visualization tools for small to moderate classification problems. Includes one-vs-one multi-class classification and a standard support vector machine baseline for comparison.

Multiple flavors of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a large choice of conditional distributions. Methods for specification, estimation, prediction, filtering, simulation, statistical testing and more. Represents a partial re-write and re-think of rugarch', making use of automatic differentiation for estimation.

This package provides a full-featured client for the Telegram MTProto protocol (<https://core.telegram.org/api>), enabling programmatic access to Telegram chats, channels, messages, media, and stories. Implements binary encoding and decoding of the Telegram TL (Type Language) schema, authentication (including two-factor), encrypted transport, and high-level helpers for downloading channel history and reactions at scale. Intended for social-science research and data collection tasks that require direct API access rather than the Bot API'.

This package provides methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.

This package provides a collection of functions used to format taxonomic names in Markdown documents. Those functions work with data structured according to Alvarez and Luebert (2018) <doi:10.3897/bdj.6.e23635>.

The textrank algorithm is an extension of the Pagerank algorithm for text. The algorithm allows to summarize text by calculating how sentences are related to one another. This is done by looking at overlapping terminology used in sentences in order to set up links between sentences. The resulting sentence network is next plugged into the Pagerank algorithm which identifies the most important sentences in your text and ranks them. In a similar way textrank can also be used to extract keywords. A word network is constructed by looking if words are following one another. On top of that network the Pagerank algorithm is applied to extract relevant words after which relevant words which are following one another are combined to get keywords. More information can be found in the paper from Mihalcea, Rada & Tarau, Paul (2004) <https://www.aclweb.org/anthology/W04-3252/>.

Calculates the community four moments (mean, variance, skewness, and kurtosis) of a given trait based on the moments described in Wieczynski et al. (2019) <doi:10.1073/pnas.1813723116>. These functional metrics are extremely useful in characterizing the distribution of traits in a plant community. It also provides tidyverse-friendly wrappers to seamlessly calculate advanced functional diversity indices (e.g., FDis, Rao's Q) using fundiversity (Grenie et al. 2023 <doi:10.1111/ecog.06585>) and functional rarity indices using funrar (Grenie et al. 2017 <doi:10.1111/ddi.12629>). Evaluating these community-weighted moments and diversity metrics allows researchers to evaluate shifts in optimal phenotypes and understand ecological filtering with exactness.

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.

Generate a palette of tints, shades or both from a single colour.

Defines the classes used to identify outliers (threshing) and compute the number of significant principal components and number of clusters (reaping) in a joint application of PCA and hierarchical clustering. See Wang et al., 2018, <doi:10.1186/s12859-017-1998-9>.

Attaches a set of packages commonly used for spatial plotting with tmap'. It includes tmap and its extensions ('tmap.glyphs', tmap.networks', tmap.cartogram', tmap.mapgl'), as well as supporting spatial data packages ('sf', stars', terra') and cols4all for exploring color palettes. The collection is designed for thematic mapping workflows and does not include the full set of packages from the R-spatial ecosystem.

This package provides a universal non-uniform random number generator for quite arbitrary distributions with piecewise twice differentiable densities.

Computes distances between taxonomic hierarchy nodes using lineage data retrieved from The Taxonomicon <http://taxonomicon.taxonomy.nl>. For distinct nodes, distance is defined as the reciprocal of the depth of their most recent common ancestor; identical nodes have distance zero. This definition yields an ultrametric within each connected hierarchy. Functions are provided for auditable name resolution, online or user-supplied lineage analysis, clade membership, pairwise and matrix distance calculation, hierarchical clustering, principal coordinates analysis, portable JSON analysis bundles, and cache management. Distance matrices are returned as base R dist objects. The distances represent classification depth rather than evolutionary time or phylogenetic branch length.