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.

Collect marketing data from TikTok Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package provides a two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, <doi:10.1093/bioinformatics/btw449>), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.

Allow to compute and visualise convective parameters commonly used in the operational prediction of severe convective storms. Core algorithm is based on a highly optimized C++ code linked into R via Rcpp'. Highly efficient engine allows to derive thermodynamic and kinematic parameters from large numerical datasets such as reanalyses or operational Numerical Weather Prediction models in a reasonable amount of time. Package has been developed since 2017 by research meteorologists specializing in severe thunderstorms. The most relevant methods used in the package based on the following publications Stipanuk (1973) <https://apps.dtic.mil/sti/pdfs/AD0769739.pdf>, McCann et al. (1994) <doi:10.1175/1520-0434(1994)009%3C0532:WNIFFM%3E2.0.CO;2>, Bunkers et al. (2000) <doi:10.1175/1520-0434(2000)015%3C0061:PSMUAN%3E2.0.CO;2>, Corfidi et al. (2003) <doi:10.1175/1520-0434(2003)018%3C0997:CPAMPF%3E2.0.CO;2>, Showalter (1953) <doi:10.1175/1520-0477-34.6.250>, Coffer et al. (2019) <doi:10.1175/WAF-D-19-0115.1>, Gropp and Davenport (2019) <doi:10.1175/WAF-D-17-0150.1>, Czernecki et al. (2019) <doi:10.1016/j.atmosres.2019.05.010>, Taszarek et al. (2020) <doi:10.1175/JCLI-D-20-0346.1>, Sherburn and Parker (2014) <doi:10.1175/WAF-D-13-00041.1>, Romanic et al. (2022) <doi:10.1016/j.wace.2022.100474>.

Standalone R implementation of habitat connectivity corridor optimization for raster and vector workflows. Supports scenario-based planning with budget-constrained optimization, optional impassable areas, packaged parity fixtures, and comparative before-and-after connectivity metrics. The package exposes structural, movement-oriented, and species-oriented strategies in a reproducible workflow aligned with a companion GIS plugin while avoiding a desktop GIS dependency.

Computes how the correlation between 2 time-series changes over time. To do so, the package follows the method from Choi & Shin (2021) <doi:10.1007/s42952-020-00073-6>. It performs a non-parametric kernel smoothing (using a common bandwidth) of all underlying components required for the computation of a correlation coefficient (i.e., x, y, x^2, y^2, xy). An automatic selection procedure for the bandwidth parameter is implemented. Alternative kernels can be used (Epanechnikov, box and normal). Both Pearson and Spearman correlation coefficients can be estimated and change in correlation over time can be tested.

Helps teachers convert existing .Rmd and .qmd teaching material into interactive tutorials for learnr or quarto-live'. Conversion preserves narrative text, setup chunks, and major chunk options, supports teacher tags, and provides explicit validation and conversion reports. Output conventions follow learnr as described by Aden-Buie et al. (2025) <doi:10.32614/CRAN.package.learnr>, quarto-live as described by Stagg (2024) <https://tidyverse.org/blog/2024/10/quarto-live-0-1-1/>, and R Markdown as described by Xie, Allaire and Grolemund (2018, ISBN:9781138359338).

An implementation of the Thornley transport resistance plant growth model. The package can be used to simulate plant growth as forced by climate system variables. The package provides methods for formatting forcing variables, simulating growth dynamics and calibrating model parameters. For more information see Higgins et al. (2025) TTR.PGM: An R package for modelling the distributions and dynamics of plants using the Thornley transport resistance plant growth model. Methods in Ecology and Evolution. in press.

This package provides a simple wrapper around the Telegram Bot API (<https://core.telegram.org/bots/api>) to access Telegram's messaging facilities with ease (e.g. you send messages, images, files from R to your smartphone).

Tracks parameter values, gradients, and Hessians at each iteration of numerical optimizers. Useful for analyzing optimization progress, diagnosing issues, and studying convergence behavior.

Trust region algorithm for nonlinear optimization. Efficient when the Hessian of the objective function is sparse (i.e., relatively few nonzero cross-partial derivatives). See Braun, M. (2014) <doi:10.18637/jss.v060.i04>.

R implementation of TFactS to predict which are the transcription factors (TFs), regulated in a biological condition based on lists of differentially expressed genes (DEGs) obtained from transcriptome experiments. This package is based on the TFactS concept by Essaghir et al. (2010) <doi:10.1093/nar/gkq149> and expands it. It allows users to perform TFactS'-like enrichment approach. The package can import and use the original catalogue file from the TFactS as well as users defined catalogues of interest that are not supported by TFactS (e.g., Arabidopsis).

Prediction intervals for ARIMA and structural time series models using importance sampling approach with uninformative priors for model parameters, leading to more accurate coverage probabilities in frequentist sense. Instead of sampling the future observations and hidden states of the state space representation of the model, only model parameters are sampled, and the method is based solving the equations corresponding to the conditional coverage probability of the prediction intervals. This makes method relatively fast compared to for example MCMC methods, and standard errors of prediction limits can also be computed straightforwardly.

Loads the 5 packages in the Tidy Consultant Universe. This collection of packages is useful for anyone doing data science, data analysis, or quantitative consulting. The functions in these packages range from data cleaning, data validation, data binning, statistical modeling, and file exporting.

Approaches for incorporating time into network analysis. Methods include: construction of time-ordered networks (temporal graphs); shortest-time and shortest-path-length analyses; resource spread calculations; data resampling and rarefaction for null model construction; reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks; vector clock latencies; and plotting functionalities. The package supports <doi:10.1371/journal.pone.0020298>.

Miscellaneous utility functions for data manipulation, data tidying, and working with gene expression data.

Fitting models for, and simulation of, trend locally stationary wavelet (TLSW) time series models, which take account of time-varying trend and dependence structure in a univariate time series. The TLSW model, and its estimation, is described in McGonigle, Killick and Nunes (2022a) <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. Further information regarding the use of the package, along with detailed examples, can be found in McGonigle, Killick and Nunes (2025) <doi:10.18637/jss.v115.i10>. New users will likely want to start with the TLSW function.

This package performs turning point and inflection point tests for U-shaped and inverse U-shaped relationships in regression models. Implements the Sasabuchi (1980) test as extended by Lind and Mehlum (2010) with support for quadratic, cubic, log-quadratic, and inverse functional forms. Features include delta-method standard errors, Fieller confidence intervals, Simonsohn (2018) two-lines test, and parametric bootstrap. Designed for post-estimation analysis of linear models, panel models, and quantile regression. References: Lind and Mehlum (2010) <doi:10.1111/j.1468-0084.2009.00569.x>; Sasabuchi (1980); Fieller (1954) <doi:10.1111/j.2517-6161.1954.tb00159.x>.

This package produces weighted cross-tabulation tables for one or more outcome variables across one or more breakdown variables, and exports them directly to Excel'. For each outcome-by-breakdown combination, the package creates a weighted percentage table and a corresponding unweighted count table, with transparent handling of missing values and light, readable formatting. Designed to support social survey analysis workflows that require large sets of consistent, publication-ready tables.

Longitudinal data offers insights into population changes over time but often requires a flexible structure, especially with varying follow-up intervals. Panel data is one way to store such records, though it adds complexity to analysis. The tvtools package for R simplifies exploring and analyzing panel data.

This package implements Time-Weighted Dynamic Time Warping (TWDTW), a measure for quantifying time series similarity. The TWDTW algorithm, described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and Maus et al. (2019) <doi:10.18637/jss.v088.i05>, is applicable to multi-dimensional time series of various resolutions. It is particularly suitable for comparing time series with seasonality for environmental and ecological data analysis, covering domains such as remote sensing imagery, climate data, hydrology, and animal movement. The twdtw package offers a user-friendly R interface, efficient Fortran routines for TWDTW calculations, flexible time weighting definitions, as well as utilities for time series preprocessing and visualization.

This package performs transformation discrimination analysis and non-transformation discrimination analysis. It also includes functions for Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Mixture Discriminant Analysis. In the context of mixture discriminant analysis, it offers options for both common covariance matrix (common sigma) and individual covariance matrices (uncommon sigma) for the mixture components.

This package provides a framework for statistical analysis in content analysis. In addition to a pipeline for preprocessing text corpora and linking to the latent Dirichlet allocation from the lda package, plots are offered for the descriptive analysis of text corpora and topic models. In addition, an implementation of Chang's intruder words and intruder topics is provided. Sample data for the vignette is included in the toscaData package, which is available on gitHub: <https://github.com/Docma-TU/toscaData>.

This package provides functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. We also provide functions for sample size determination and estimation of diagnostic accuracy measures. We also include graphical tools. The statistical methodology used here can be found in Perez-Jaume et al (2017) <doi:10.18637/jss.v082.i04> and in Skaltsa et al (2010, 2012) <doi:10.1002/bimj.200900294>, <doi:10.1002/sim.4369>.