Compile Typst files using the typst-cli (<https://typst.app>) command line tool. Automatically falls back to rendering via embedded Typst from Quarto (<https://quarto.org>) if Typst is not installed. Includes utilities to check for typst-cli availability and run Typst commands.

To handle higher-order tensor data. See Kolda and Bader (2009) <doi:10.1137/07070111X> for details on tensor. While existing packages on tensor data extend the base array class to some data classes, this package serves as an alternative resort to handle tensor only as array class. Some functionalities related to missingness are also supported.

High-resolution movement-sensor tags typically include accelerometers to measure body posture and sudden movements or changes in speed, magnetometers to measure direction of travel, and pressure sensors to measure dive depth in aquatic or marine animals. The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; calibration (from raw data to calibrated data in scientific units); visualization (for example, multi-panel time-series plots); data processing (such as event detection, calculation of derived metrics like jerk and dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis).

Statistics students often have problems understanding the relation between a random variable's true scale and its z-values. To allow instructors to better better visualize histograms for these students, the package provides histograms with two horizontal axis containing z-values and the true scale of the variable. The function TeachHistDens() provides a density histogram with two axis. TeachHistCounts() and TeachHistRelFreq() are variations for count and relative frequency histograms, respectively. TeachConfInterv() and TeachHypTest() help instructors to visualize confidence levels and the results of hypothesis tests.

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.

Efficient tabulation with Stata-like output. For each unique value of the variable, it shows the number of observations with that value, proportion of observations with that value, and cumulative proportion, in descending order of frequency. Accepts data.table, tibble, or data.frame as input. Efficient with big data: if you give it a data.table, tab() uses data.table syntax.

Pure R implementation of Apache Thrift. This library doesn't require any code generation. To learn more about Thrift go to <https://thrift.apache.org>.

Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) <doi:10.1561/2200000073> for the general exposition to the study of computational optimal transport.

This package performs Thresholded Ordered Sparse Canonical Correlation Analysis (CCA). For more details see Senar, N. (2024) <doi:10.1093/bioadv/vbae021> and Senar, N. et al. (2025) <doi:10.48550/arXiv.2503.15140>.

Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.

This package provides R Markdown output formats to use Tufte styles for PDF and HTML output.

Visualize your Tidyverse data analysis pipelines via the Tidy Data Tutor'(<https://tidydatatutor.com/>) web application.

Assists performing tip-dating of phylogenetic trees with BEAST BEAST is a popular software for phylogenetic analysis. The package assists the implementation of various phylogenetic tip- dating tests using BEAST. It contains two main functions. The first one allows preparing date randomization analyses, which assess the temporal signal of a data set. The second function allows performing leave-one-out analyses, which test for the consistency between independent calibration sequences and allow pinpointing those leading to potential bias. The included tutorial provides detailed step-by-step instructions. An expanded description of the package can be found in article: Rieux, A. and Khatchikian, C.E. (2017), TIPDATINGBEAST: an R package to assist the implementation of phylogenetic tip-dating tests using BEAST. Molecular Ecology Resources, 17: 608-613. <onlinelibrary.wiley.com/doi/full/10.1111/1755-0998.12603>.

Evaluate inline or chunks of R code in template files and replace with their output modifying the resulting template.

This package provides a toolkit for calculating topographic distances and identifying and plotting topographic paths. Topographic distances can be calculated along shortest topographic paths (Wang (2009) <doi:10.1111/j.1365-294X.2009.04338.x>), weighted topographic paths (Zhan et al. (1993) <doi:10.1007/3-540-57207-4_29>), and topographic least cost paths (Wang and Summers (2010) <doi:10.1111/j.1365-294X.2009.04465.x>). Functions can map topographic paths on colored or hill shade maps and plot topographic cross sections (elevation profiles) for the paths.

This package provides a set of fast tidy functions for wrangling, completing and summarising date and date-time data. It combines tidyverse syntax with the efficiency of data.table and speed of collapse'.

An implementation that combines trait data and a phylogenetic tree (or trees) into a single object of class treedata.table'. The resulting object can be easily manipulated to simultaneously change the trait- and tree-level sampling. Currently implemented functions allow users to use a data.table syntax when performing operations on the trait dataset within the treedata.table object. For more details see Roman-Palacios et al. (2021) <doi:10.7717/peerj.12450>.

Testing for trajectory presence and heterogeneity on multivariate data. Two statistical methods (Tenha & Song 2022) <doi:10.1371/journal.pcbi.1009829> are implemented. The tree dimension test quantifies the statistical evidence for trajectory presence. The subset specificity measure summarizes pattern heterogeneity using the minimum subtree cover. There is no user tunable parameters for either method. Examples are included to illustrate how to use the methods on single-cell data for studying gene and pathway expression dynamics and pathway expression specificity.

Simple tabulation should be dead simple. This package is an opinionated approach to easy tabulations while also providing exact numbers and allowing for re-usability. This is achieved by providing tabulations as data.frames with columns for values, optional variable names, frequency counts including and excluding NAs and percentages for counts including and excluding NAs. Also values are automatically sorted by in decreasing order of frequency counts to allow for fast skimming of the most important information.

Fit of a double additive cure survival model with time-varying covariates. The additive terms in the long- and short-term survival submodels, modelling the cure probability and the event timing for susceptible units, are estimated using Laplace P-splines. For more details, see Lambert and Kreyenfeld (2025) <doi:10.1093/jrsssa/qnaf035>.

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.

Assists in the TOPSIS analysis process, designed to return at the end of the answer of the TOPSIS multicriteria analysis, a ranking table with the best option as the analysis proposes. TOPSIS is basically a technique developed by Hwang and Yoon in 1981, starting from the point that the best alternative should be closest to the positive ideal solution and farthest from the negative one, based on several criteria to result in the best benefit. (LIU, H. et al., 2019) <doi:10.1016/j.agwat.2019.105787>.

This package provides functions for estimating mediation effects that vary over time as described in Cai X, Coffman DL, Piper ME, Li R. Estimation and inference for the mediation effect in a time-varying mediation model. BMC Med Res Methodol. 2022;22(1):1-12.

This package provides utility functions for plotting. Includes functions for color manipulation, plot customization, panel size control, data optimization for plots, and layout adjustments.