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

This package provides a plug-in for the text mining framework tm to support text mining in a distributed way. The package provides a convenient interface for handling distributed corpus objects based on distributed list objects.

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.

This package provides a framework for high-dimensional mediation analysis using transfer learning. The main function TransHDM() integrates large-scale source data to improve the detection power of potential mediators in small-sample target studies. It addresses data heterogeneity via transfer regularization and debiased estimation while controlling the false discovery rate. The package also includes utilities for data generation (gen_simData_homo(), gen_simData_hetero()), baseline methods such as lasso() and dblasso(), sure independence screening via SIS(), and model diagnostics through source_detection(). The methodology is described in Pan et al. (2025) <doi:10.1093/bib/bbaf460>.

In Cox's proportional hazard model, covariates are modeled as linear function and may not be flexible. This package implements additive trend filtering Cox proportional hazards model as proposed in Jiacheng Wu & Daniela Witten (2019) "Flexible and Interpretable Models for Survival Data", Journal of Computational and Graphical Statistics, <DOI:10.1080/10618600.2019.1592758>. The fitted functions are piecewise polynomial with adaptively chosen knots.

This package provides functionality for designing and analysing two-phase genetic association studies. Phase 1 data usually come from genome-wide association study (GWAS) results and we assume phase 2 data will be part of a targeted genome sequencing or fine-mapping study. At design stage, the package assists in selecting a subset of individuals that will be sequenced for phase 2 via alternative approaches, including a flexible genetic algorithm (GA) for near-optimal designs. Once phase 2 data have been collected, the package implements methods to analyse phase 1 and phase 2 data together using semi-parametric regression models via the expectation-maximization (EM) algorithm. For more details see Espin-Garcia, Craiu and Bull (2018) <doi:10.1002/gepi.22099> and Espin-Garcia, Craiu and Bull (2021) <doi:10.1002/sim.9211>.

Enables all rstan functionality for a TMB model object, in particular MCMC sampling and chain visualization. Sampling can be performed with or without Laplace approximation for the random effects. This is demonstrated in Monnahan & Kristensen (2018) <DOI:10.1371/journal.pone.0197954>.

This package implements the tree-spatial scan statistic for detecting clusters that combine both spatial and hierarchical structures, as proposed by Cancado et al. (2025) <doi:10.1007/s10651-025-00670-w>. The method extends Kulldorff (1997) <doi:10.1080/03610929708831995> circular spatial scan statistic and the tree-based scan statistic of Kulldorff et al. (2003) <doi:10.1111/1541-0420.00039> by searching for anomalies in both geographic regions and branches of hierarchical trees simultaneously. The package also provides standalone implementations of Kulldorff's circular spatial scan statistic and the tree-based scan statistic. Statistical significance is assessed via Monte Carlo simulation under a Poisson or binomial model, with optional OpenMP parallelization.

This package provides tools to calculate trait probability density functions (TPD) at any scale (e.g. populations, species, communities). TPD functions are used to compute several indices of functional diversity, as well as its partition across scales. These indices constitute a unified framework that incorporates the underlying probabilistic nature of trait distributions into uni- or multidimensional functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for further information.

This package provides a tbl_ts class (the tsibble') for temporal data in an data- and model-oriented format. The tsibble provides tools to easily manipulate and analyse temporal data, such as filling in time gaps and aggregating over calendar periods.

This package provides functions for point and interval estimation in error-in-variables models via total least squares or generalized total least squares method. See Golub and Van Loan (1980) <doi:10.1137/0717073>, Gleser (1981) <https://www.jstor.org/stable/2240867>, Ivan Markovsky and Huffel (2007) <doi:10.1016/j.sigpro.2007.04.004> for more information.

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.

This package implements Bayesian methods, described in Hugh-Jones (2019) <doi:10.1007/s40881-019-00069-x>, for estimating the proportion of liars in coin flip-style experiments, where subjects report a random outcome and are paid for reporting a "good" outcome.

An easy way to examine archaeological count data. This package provides several tests and measures of diversity: heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.), richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and similarity (Brainerd-Robinson, etc.). It allows to easily visualize count data and statistical thresholds: rank vs abundance plots, heatmaps, Ford (1962) and Bertin (1977) diagrams, etc.

Converting structured data from tables into XML format using predefined templates ensures consistency and flexibility, making it ideal for data exchange, reporting, and automated workflows.

This package provides datasets in a format that can be easily consumed by torch dataloaders'. Handles data downloading from multiple sources, caching and pre-processing so users can focus only on their model implementations.

Finding the best values for user-specified arguments of a prediction algorithm can be difficult, particularly if there is an interaction between argument levels. This package automates the testing of any user-defined prediction algorithm over an arbitrary number of arguments. It includes functions for testing the algorithm over the given arguments with respect to an arbitrary number of user-defined diagnostics, visualising the results of these tests, and finding the optimal argument combinations with respect to each diagnostic.

This package provides tools to deploy TensorFlow <https://www.tensorflow.org/> models across multiple services. Currently, it provides a local server for testing cloudml compatible services.

The Tanaka method enhances the representation of topography on a map using shaded contour lines. In this simplified implementation of the method, north-west white contours represent illuminated topography and south-east black contours represent shaded topography. See Tanaka (1950) <doi:10.2307/211219>.

This package provides functions to get personal Google Scholar profile data from web API and show it in table or figure format.

Calculates the number of true positives and false positives from a dataset formatted for Jackknife alternative free-response receiver operating characteristic which is used for statistical analysis which is explained in the book Chakraborty DP (2017), "Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples", Taylor-Francis <https://www.crcpress.com/9781482214840>.

This package provides a version of the Titanic survival data tailored for people analytics demonstrations and practice. While another package, titanic', reproduces the Kaggle competition files with minimal preprocessing, tidytitanic combines the train and test datasets into the single dataset, passengers', for exploration and summary across all passengers. It also extracts personal identifiersâ such as first names, last names, and titles from the raw name field, enabling demographic analysis. The passengers data does not cover the crew, but this package also provides the more bare-bones, crew-containing datasets tidy_titanic and flat_titanic based on the Titanic data set from datasets for further exploration. This human-centered data package is designed to support exploratory data analysis, feature engineering, and pedagogical use cases.

This package implements measures of tree similarity, including information-based generalized Robinson-Foulds distances (Phylogenetic Information Distance, Clustering Information Distance, Matching Split Information Distance; Smith 2020) <doi:10.1093/bioinformatics/btaa614>; Jaccard-Robinson-Foulds distances (Bocker et al. 2013) <doi:10.1007/978-3-642-40453-5_13>, including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>; the Matching Split Distance (Bogdanowicz & Giaro 2012) <doi:10.1109/TCBB.2011.48>; the Hierarchical Mutual Information (Perotti et al. 2015) <doi:10.1103/PhysRevE.92.062825>; Maximum Agreement Subtree distances; the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the Nearest Neighbour Interchange (NNI) distance, approximated per Li et al. (1996) <doi:10.1007/3-540-61332-3_168>. Includes tools for visualizing mappings of tree space (Smith 2022) <doi:10.1093/sysbio/syab100>, for identifying islands of trees (Silva and Wilkinson 2021) <doi:10.1093/sysbio/syab015>, for calculating the median of sets of trees, and for computing the information content of trees and splits.

Estimators for semi-parametric linear regression models with truncated response variables (fixed truncation point). The estimators implemented are the Symmetrically Trimmed Least Squares (STLS) estimator introduced by Powell (1986) <doi:10.2307/1914308>, the Quadratic Mode (QME) estimator introduced by Lee (1993) <doi:10.1016/0304-4076(93)90056-B>, and the Left Truncated (LT) estimator introduced by Karlsson (2006) <doi:10.1007/s00184-005-0023-x>.