R spatial objects for Tilegrams. Tilegrams are tiled maps where the region size is proportional to the certain characteristics of the dataset.

This package provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive. For details of TRR, see Li L, Zhang X (2017) <doi:10.1080/01621459.2016.1193022>. For details of TPR, see Zhang X, Li L (2017) <doi:10.1080/00401706.2016.1272495>. For details of 1D algorithm, see Cook RD, Zhang X (2016) <doi:10.1080/10618600.2015.1029577>. For details of ECD algorithm, see Cook RD, Zhang X (2018) <doi:10.5705/ss.202016.0037>. For more details of the package, see Zeng J, Wang W, Zhang X (2021) <doi:10.18637/jss.v099.i12>.

Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft <doi:10.18637/jss.v055.i08>.).

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.

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.

Does nothing useful, but perhaps does that nothing in an entertaining or informative fashion.

Simulation, estimation and inference for univariate and multivariate TV(s)-GARCH(p,q,r)-X models, where s indicates the number and shape of the transition functions, p is the ARCH order, q is the GARCH order, r is the asymmetry order, and X indicates that covariates can be included; see Campos-Martins and Sucarrat (2024) <doi:10.18637/jss.v108.i09>. In the multivariate case, variances are estimated equation by equation and dynamic conditional correlations are allowed. The TV long-term component of the variance as in the multiplicative TV-GARCH model of Amado and Terasvirta (2013) <doi:10.1016/j.jeconom.2013.03.006> introduces non-stationarity whereas the GARCH-X short-term component describes conditional heteroscedasticity. Maximisation by parts leads to consistent and asymptotically normal estimates.

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.

Transfer learning for generalized factor models with support for continuous, count (Poisson), and binary data types. The package provides functions for single and multiple source transfer learning, source detection to identify positive and negative transfer sources, factor decomposition using Maximum Likelihood Estimation (MLE), and information criteria ('IC1 and IC2') for rank selection. The methods are particularly useful for high-dimensional data analysis where auxiliary information from related source datasets can improve estimation efficiency in the target domain.

This package provides functions to construct two-phase design layouts, compute treatment- and block-incidence matrices, derive C-matrices for residual, direct, and interaction effects, and calculate the efficiency factor for two-phase experimental designs with factorial treatment structure.

This package provides tools to work with template code and text in R. It aims to provide a simple substitution mechanism for R-expressions inside these templates. Templates can be written in other languages like SQL', can simply be represented by characters in R, or can themselves be R-expressions or functions.

Matching terminal restriction fragment length polymorphism ('TRFLP') profiles between unknown samples and a database of known samples. TRAMPR facilitates analysis of many unknown profiles at once, and provides tools for working directly with electrophoresis output through to generating summaries suitable for community analyses with R's rich set of statistical functions. TRAMPR also resolves the issues of multiple TRFLP profiles within a species, and shared TRFLP profiles across species.

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.

Translate R control flow expressions into Tensorflow graphs.

Link R with Transformers from Hugging Face to transform text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see <https://www.r-text.org>.

Triad Log-Linear modelling of Imprinting Environmental interactions, and Maternal effects (TriLLIEM). This is an implementation of the log-linear model described in a series of papers, see for example Ainsworth et al. (2010) <doi:10.1002/gepi.20547>.

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>.

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>.

The total deviation index (TDI) is an unscaled statistical measure used to evaluate the deviation between paired quantitative measurements when assessing the extent of agreement between different raters. It describes a boundary such that a large specified proportion of the differences in paired measurements are within the boundary (Lin, 2000) <https://pubmed.ncbi.nlm.nih.gov/10641028/>. This R package implements some methodologies existing in the literature for TDI estimation and inference in the case of two raters.

Determine the path of the executing script. Compatible with several popular GUIs: Rgui', RStudio', Positron', VSCode', Jupyter', Emacs', and Rscript (shell). Compatible with several functions and packages: source()', sys.source()', debugSource() in RStudio', compiler::loadcmp()', utils::Sweave()', box::use()', knitr::knit()', plumber::plumb()', shiny::runApp()', package:targets', and testthat::source_file()'.

This package provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.

Fast randomization based two sample tests. Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at <arXiv:2007.01360> or <https://codowd.com/public/DTS.pdf> for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others.

This is a small package to provide consistent tick marks for plotting ggplot2 figures. It provides breaks and labels for ggplot2 without requiring ggplot2 to be installed.

Efficient method for fitting nonparametric matrix trace regression model. The detailed description can be found in C. Lee, L. Li, H. Zhang, and M. Wang (2021). Nonparametric Trace Regression via Sign Series Representation. <arXiv:2105.01783>. The method employs the aggregation of structured sign series for trace regression (ASSIST) algorithm.