Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.

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.

Computation of effects under linear, logistic and Poisson regression models with transformed variables. Logarithm and power transformations are allowed. Effects can be displayed both numerically and graphically in both the original and the transformed space of the variables. The methods are described in Barrera-Gomez and Basagana (2015) <doi:10.1097/EDE.0000000000000247>.

Computation of stopping boundaries for a single-arm trial using a Bayesian criterion; i.e., for each m<=n (n= total patient number of the trial) the smallest number of observed toxicities is calculated leading to the termination of the trial/accrual according to the specified criteria. The probabilities of stopping the trial/accrual at and up until (resp.) the m-th patient (m<=n) is also calculated. This design is more conservative than the frequentist approach (using Clopper Pearson CIs) which might be preferred as it concerns safety.See also Aamot et.al.(2010) "Continuous monitoring of toxicity in clinical trials - simulating the risk of stopping prematurely" <doi:10.5414/cpp48476>.

Test functions are often used to test computer code. They are used in optimization to test algorithms and in metamodeling to evaluate model predictions. This package provides test functions that can be used for any purpose.

This package provides methods for computing joint tests, controlling the Familywise Error Rate (FWER) and getting lower bounds on the number of false hypotheses in a set. The methods implemented here are described in Mogensen and Markussen (2021) <doi:10.48550/arXiv.2108.04731>.

This package provides an interactive interface to the tfrmt package. Users can import, modify, and export tables and templates with little to no code.

Inferring causation from time series data through empirical dynamic modeling (EDM), with methods such as convergent cross mapping from Sugihara et al. (2012) <doi:10.1126/science.1227079>, partial cross mapping as outlined in Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality as described in Tao et al. (2023) <doi:10.1016/j.fmre.2023.01.007>.

Enables the acquisition of Korean financial market data, designed to integrate seamlessly with the tidyquant package.

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.

This package implements the TRUH test statistic for two sample testing under heterogeneity. TRUH incorporates the underlying heterogeneity and imbalance in the samples, and provides a conservative test for the composite null hypothesis that the two samples arise from the same mixture distribution but may differ with respect to the mixing weights. See Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee Ann. Appl. Stat. 14(4): 1777-1805 (December 2020). <DOI:10.1214/20-AOAS1362> for more details.

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.

Tsallis distribution also known as the q-exponential family distribution. Provide distribution d, p, q, r functions, fitting and testing functions. Project initiated by Paul Higbie and based on Cosma Shalizi's code.

This package contains some auxiliary functions.

Translate R control flow expressions into Tensorflow graphs.

Collect your data on digital marketing campaigns from Twitter Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package provides a wrapper to a set of algorithms designed to recognise positional cues present in hierarchical for-human Tables (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into machine-readable LongForm Dataframes.

Instead of nesting function calls, annotate and transform functions using "#." comments.

Interface to the API for TreeBASE <http://treebase.org> from R. TreeBASE is a repository of user-submitted phylogenetic trees (of species, population, or genes) and the data used to create them.

Finds the posterior modes for the mean and standard deviation for a truncated normal distribution with one or two known truncation points. The method used extends Bayesian methods for parameter estimation for a singly truncated normal distribution under the Jeffreys prior (see Zhou X, Giacometti R, Fabozzi FJ, Tucker AH (2014). "Bayesian estimation of truncated data with applications to operational risk measurement". <doi:10.1080/14697688.2012.752103>). This package additionally allows for a doubly truncated normal distribution.

This package provides a robust computational framework for analyzing complex multimodal data. Extends existing state-dependent models to account for diverse data streams, addressing challenges such as varying temporal scales and learner characteristics to improve the robustness and interpretability of findings. For methodological details, see Shaffer, Wang, and Ruis (2025) "Transmodal Analysis" <doi:10.18608/jla.2025.8423>.

Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the mgcv package to specify splines via the formula interface. See Thorson et al. (2025) <doi:10.1111/geb.70035> for more details.

It offers functions for splitting, parsing, tokenizing and creating a vocabulary for big text data files. Moreover, it includes functions for building a document-term matrix and extracting information from those (term-associations, most frequent terms). It also embodies functions for calculating token statistics (collocations, look-up tables, string dissimilarities) and functions to work with sparse matrices. Lastly, it includes functions for Word Vector Representations (i.e. GloVe', fasttext') and incorporates functions for the calculation of (pairwise) text document dissimilarities. The source code is based on C++11 and exported in R through the Rcpp', RcppArmadillo and BH packages.

Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques.