This package infers the V genotype of an individual from immunoglobulin (Ig) repertoire sequencing data (AIRR-Seq, Rep-Seq). Includes detection of any novel alleles. This information is then used to correct existing V allele calls from among the sample sequences. Citations: Gadala-Maria, et al (2015) <doi:10.1073/pnas.1417683112>, Gadala-Maria, et al (2019) <doi:10.3389/fimmu.2019.00129>.

This package provides functions to access the database of 217 data-frames with aggregate study-level characteristics (that may act as effect modifiers) extracted from published systematic reviews with network meta-analysis. The database shall only be used for developing and appraising the methodology to assess the transitivity assumption quantitatively.

This package provides convenience functions for common data modification and analysis tasks in communication research. This includes functions for univariate and bivariate data analysis, index generation and reliability computation, and intercoder reliability tests. All functions follow the style and syntax of the tidyverse, and are construed to perform their computations on multiple variables at once. Functions for univariate and bivariate data analysis comprise summary statistics for continuous and categorical variables, as well as several tests of bivariate association including effect sizes. Functions for data modification comprise index generation and automated reliability analysis of index variables. Functions for intercoder reliability comprise tests of several intercoder reliability estimates, including simple and mean pairwise percent agreement, Krippendorff's Alpha (Krippendorff 2004, ISBN: 9780761915454), and various Kappa coefficients (Brennan & Prediger 1981 <doi: 10.1177/001316448104100307>; Cohen 1960 <doi: 10.1177/001316446002000104>; Fleiss 1971 <doi: 10.1037/h0031619>).

An implementation of tidy speaker vowel normalization. This includes generic functions for defining new normalization methods for points, formant tracks, and Discrete Cosine Transform coefficients, as well as convenience functions implementing established normalization methods. References for the implemented methods are: Johnson, Keith (2020) <doi:10.5334/labphon.196> Lobanov, Boris (1971) <doi:10.1121/1.1912396> Nearey, Terrance M. (1978) <https://sites.ualberta.ca/~tnearey/Nearey1978_compressed.pdf> Syrdal, Ann K., and Gopal, H. S. (1986) <doi:10.1121/1.393381> Watt, Dominic, and Fabricius, Anne (2002) <https://www.latl.leeds.ac.uk/article/evaluation-of-a-technique-for-improving-the-mapping-of-multiple-speakers-vowel-spaces-in-the-f1-f2-plane/>.

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.

Provide a range of functions with multiple criteria for cutting phylogenetic trees at any evolutionary depth. It enables users to cut trees in any orientation, such as rootwardly (from root to tips) and tipwardly (from tips to its root), or allows users to define a specific time interval of interest. It can also be used to create multiple tree pieces of equal temporal width. Moreover, it allows the assessment of novel temporal rates for various phylogenetic indexes, which can be quickly displayed graphically.

Converting text to numerical features requires specifically created procedures, which are implemented as steps according to the recipes package. These steps allows for tokenization, filtering, counting (tf and tfidf) and feature hashing.

Create a time-varying dataset using features, exposure, and look back specifications.

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/>.

Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior.

This package provides a model for the growth of self-limiting populations using three, four, or five parameter functions, which have wide applications in a variety of fields. The dependent variable in a dynamical modeling could be the population size at time x, where x is the independent variable. In the analysis of quantitative polymerase chain reaction (qPCR), the dependent variable would be the fluorescence intensity and the independent variable the cycle number. This package then would calculate the TWW cycle threshold.

This package provides wrapper functions to the multiple marginal model function mmm() of package multcomp to implement the trend test of Tukey, Ciminera and Heyse (1985) <DOI:10.2307/2530666> for general parametric models.

Calculates empirical TL-moments (trimmed L-moments) of arbitrary order and trimming, and converts them to distribution parameters.

This package provides a Tcl/Tk Graphical User Interface (GUI) to display images than can be zoomed and panned using the mouse and keyboard shortcuts. tkImgR read and write different image formats (PPM/PGM, PNG and GIF) using the standard Tcl/Tk distribution (>=8.6), but other formats (JPEG, TIFF, CR2) can be handled using the tkImg package for Tcl/Tk'.

Simulates typing of R script files for presentations and demonstrations. Provides character-by-character animation with optional live code execution. Supports R scripts (.R), R Markdown (.Rmd), and Quarto (.qmd) documents.

Overall predictive performance is measured by a mean score (or loss), which decomposes into miscalibration, discrimination, and uncertainty components. The main focus is visualization of these distinct and complementary aspects in joint displays. See Dimitriadis, Gneiting, Jordan, Vogel (2024) <doi:10.1016/j.ijforecast.2023.09.007>.

This package provides a toolkit of tidy data manipulation verbs with data.table as the backend. Combining the merits of syntax elegance from dplyr and computing performance from data.table', tidyfst intends to provide users with state-of-the-art data manipulation tools with least pain. This package is an extension of data.table'. While enjoying a tidy syntax, it also wraps combinations of efficient functions to facilitate frequently-used data operations.

Transformer is a Deep Neural Network Architecture based i.a. on the Attention mechanism (Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>).

This package provides a coherent interface for evaluating models fit with the trending package. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.

Routines for nonlinear time series analysis based on Threshold Autoregressive Moving Average (TARMA) models. It provides functions and methods for: TARMA model fitting and forecasting, including robust estimators, see Goracci et al. JBES (2025) <doi:10.1080/07350015.2024.2412011>; tests for threshold effects, see Giannerini et al. JoE (2024) <doi:10.1016/j.jeconom.2023.01.004>, Goracci et al. Statistica Sinica (2023) <doi:10.5705/ss.202021.0120>, Angelini et al. (2024) <doi:10.48550/arXiv.2308.00444>; unit-root tests based on TARMA models, see Chan et al. Statistica Sinica (2024) <doi:10.5705/ss.202022.0125>.

An integrated suite of tools for creating, maintaining, and reusing FAIR (Findable, Accessible, Interoperable, Reusable) theories. Designed to support transparent and collaborative theory development, the package enables users to formalize theories, track changes with version control, assess pre-empirical coherence, and derive testable hypotheses. Aligning with open science principles and workflows, theorytools facilitates the systematic improvement of theoretical frameworks and enhances their discoverability and usability.

Implementation of the transformation of the Mean Opinion Scores (MOS) to be used before applying the rank based statistical techniques. The method and its necessity is described in: Babak Naderi, Sebastian Möller (2020) <arXiv:2004.11490>.

This package provides a complete data set of historic GB trig points in British National Grid (OSGB36) coordinate reference system. Trig points (aka triangulation stations) are fixed survey points used to improve the accuracy of map making in Great Britain during the 20th Century. Trig points are typically located on hilltops so still serve as a useful navigational aid for walkers and hikers today.

Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.