Time series toolkit with identical behavior for all time series classes: ts','xts', data.frame', data.table', tibble', zoo', timeSeries', tsibble', tis or irts'. Also converts reliably between these classes.

It analyzes text to create a count of top n-grams, including tokens (one-word), bigrams(two-word), and trigrams (three-word), while removing all stopwords. It also plots the n-grams and corresponding counts as a bar chart.

Data filtering module for teal applications. Allows for interactive filtering of data stored in data.frame and MultiAssayExperiment objects. Also displays filtered and unfiltered observation counts.

Operators and functions provided by base R sometimes lack some features found in other programming languages, such as the ability to concatenate strings using + or to repeat strings using *. This package aims at providing such functionality without breaking existing code, i.e., only statements, that would throw errors in pure base R are patched.

Deconvolving thermoluminescence glow curves according to various kinetic models (first-order, second-order, general-order, and mixed-order) using a modified Levenberg-Marquardt algorithm (More, 1978) <DOI:10.1007/BFb0067700>. It provides the possibility of setting constraints or fixing any of parameters. It offers an interactive way to initialize parameters by clicking with a mouse on a plot at positions where peak maxima should be located. The optimal estimate is obtained by "trial-and-error". It also provides routines for simulating first-order, second-order, and general-order glow peaks.

Computes a point pattern in R^2 or on a graph that is representative of a collection of many data patterns. The result is an approximate barycenter (also known as Fréchet mean or prototype) based on a transport-transform metric. Possible choices include Optimal SubPattern Assignment (OSPA) and Spike Time metrics. Details can be found in Müller, Schuhmacher and Mateu (2020) <doi:10.1007/s11222-020-09932-y>.

This package provides a framework for the creation and use of Neural ordinary differential equations with the tensorflow and keras packages. The idea of Neural ordinary differential equations comes from Chen et al. (2018) <doi:10.48550/arXiv.1806.07366>, and presents a novel way of learning and solving differential systems.

An easy way to examine archaeological count data. This package provides several tests and measures of diversity: heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.), richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and similarity (Brainerd-Robinson, etc.). It allows to easily visualize count data and statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams, etc.

The twelvedata REST service offers access to current and historical data on stocks, standard as well as digital crypto currencies, and other financial assets covering a wide variety of course and time spans. See <https://twelvedata.com/> for details, to create an account, and to request an API key for free-but-capped access to the data.

Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <DOI:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.

This package provides functions that can be used to calculate time-dependent state and parameter sensitivities for both continuous- and discrete-time deterministic models. See Ng et al. (2023) <doi:10.1086/726143> for more information about time-dependent sensitivity analysis.

This package implements the Transcendental Algorithm for Mixtures of Distributions (TAMD), a penalized likelihood framework for fitting finite Gaussian mixture models. TAMD augments the Expectation-Maximization (EM) algorithm with analytic barrier terms built from the Hellinger affinity that diverge on the singular locus, actively preventing component coalescence and weight degeneracy. Provides the core TAMD fitting function, closed-form Hellinger affinity and gradient computations, the Transcendental Affinity Criterion (TAC) for geometry-aware model selection, the regularity index rho (a scalar diagnostic for mixture fit quality), and reproduction scripts for all simulation studies. Methods are described in Fokoue (2024) <doi:10.48550/arXiv.2602.03889>. See also Titterington, Smith and Makov (1985, ISBN:0-471-90510-4) and Watanabe (2009, ISBN:978-0-521-86408-7).

Likelihood ratio and maximum likelihood statistics are provided that can be used as alternatives to p-values Colquhoun (2017) <doi:10.1098/rsos.171085>. Arguments can be either p-values or t-statistics. together with degrees of freedom. For the function tTOlr', the argument twoSided has the default twoSided = TRUE'.

This package provides functions for interacting directly with the Taiwan Economic Journal API to offer data in R. For more information go to <https://api.tej.com.tw>.

Trusted Timestamps (tts) are created by incorporating a hash of a file or dataset into a transaction on the decentralized blockchain (Stellar network). The package makes use of a free service provided by <https://stellarapi.io>.

Articles in the R Journal were first authored in LaTeX', which performs admirably for PDF files but is less than ideal for modern online interfaces. The texor package does all the transitional chores and conversions necessary to move to the online versions.

Cluster data without specifying the number of clusters using the Table Invitation Prior (TIP) introduced in the paper "Clustering Gene Expression Using the Table Invitation Prior" by Charles W. Harrison, Qing He, and Hsin-Hsiung Huang (2022) <doi:10.3390/genes13112036>. TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors.

Implementation of Testlet and Item Response Theory. A light-version yet comprehensive and streamlined framework for psychometric analysis using unidimensional and multidimensional Item Response Theory (IRT; Baker & Kim (2004) <doi:10.1201/9781482276725>) and Testlet Response Theory (TRT; Wainer et al., (2007) <doi:10.1017/CBO9780511618765>). Designed for researchers, this package supports the estimation of item and person parameters for a wide variety of models, including binary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter Logistic) and polytomous (Partial Credit Model, Generalized Partial Credit Model, Graded Response Model) formats. It also supports the estimation of Testlet models (Rasch Testlet, 2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet, Bifactor, Partial Credit Model Testlet, Graded Response), allowing users to account for local item dependence in bundled items. A key feature is the specialized support for combination use and joint estimation of item response model and testlet response model in one calibration. Beyond standard estimation via Marginal Maximum Likelihood with Expectation-Maximization (EM) or Joint Maximum Likelihood, the package also offers Bayesian estimation using priors with maximum a posteriori (MAP) method for unidimensional item response theory models. It also provides functions for scale linking and equating (Mean-Mean, Mean-Sigma, Stocking-Lord) to ensure comparability across mixed-format test forms. It also facilitates fixed-parameter calibration, enabling users to estimate person abilities with known item parameters or vice versa, which is essential for pre-equating studies and item bank maintenance. Comprehensive data simulation functions are included to generate synthetic datasets with complex structures, including mixed-model blocks and specific testlet effects, aiding in methodological research and study design validation. Researchers can try multiple simulation situations.

This package contains R functions for simulating and estimating integer-valued trawl processes as described in the article Veraart (2019),"Modeling, simulation and inference for multivariate time series of counts using trawl processes", Journal of Multivariate Analysis, 169, pages 110-129, <doi:10.1016/j.jmva.2018.08.012> and for simulating random vectors from the bivariate negative binomial and the bi- and trivariate logarithmic series distributions.

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.

Travel Time API <https://docs.traveltime.com/api/overview/introduction> helps users find locations by journey time rather than using â as the crow fliesâ distance. Time-based searching gives users more opportunities for personalisation and delivers a more relevant search.

This package provides bindings to Tree-sitter', an incremental parsing system for programming tools. Tree-sitter builds concrete syntax trees for source files of any language, and can efficiently update those syntax trees as the source file is edited. It also includes a robust error recovery system that provides useful parse results even in the presence of syntax errors.

This package provides a shiny based interactive exploration framework for analyzing clinical trials data. teal currently provides a dynamic filtering facility and different data viewers. teal shiny applications are built using standard shiny modules.

Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data.