The trigger strategy is a general framework for a multistage statistical design with multiple hypotheses, allowing an adaptive selection of interim analyses. The selection of interim stages can be associated with some prespecified endpoints which serve as the trigger. This selection allows us to refine the critical boundaries in hypotheses testing procedures, and potentially increase the statistical power. This package includes several trial designs using the trigger strategy. See Gou, J. (2023), "Trigger strategy in repeated tests on multiple hypotheses", Statistics in Biopharmaceutical Research, 15(1), 133-140, and Gou, J. (2022), "Sample size optimization and initial allocation of the significance levels in group sequential trials with multiple endpoints", Biometrical Journal, 64(2), 301-311.

Accurately estimates phase shifts by accounting for period changes and for the point in the circadian cycle at which the stimulus occurs. See Tackenberg et al. (2018) <doi:10.1177/0748730418768116>.

Draws tornado plots for model sensitivity to univariate changes. Implements methods for many modeling methods including linear models, generalized linear models, survival regression models, and arbitrary machine learning models in the caret package. Also draws variable importance plots.

Class definitions and constructors for pseudo-vectors containing all permutations, combinations and subsets of objects taken from a vector. Simplifies working with structures commonly encountered in combinatorics.

Compute and visualize the Tissot Indicatrix for map projections. The indicatrix characterizes projection distortion by computing scale factors, angular deformation, areal distortion, and convergence at arbitrary points. Based on the calculations shared by Bill Huber on <https://gis.stackexchange.com/a/5075/482>. Uses GDAL for coordinate transformation. Developed using the method published in Snyder, JP (1987) <doi:10.3133/pp1395>.

This package provides tools for specifying time series regression models.

Render clinical submission tables and listings to RTF', LaTeX', HTML', PDF', and DOCX from pre-summarised data frames, with no external Java or SAS dependency. Features include decimal alignment via font metrics, multi-level column headers with passthrough leaves, predicate-targeted cell styling, footnotes, and group-aware pagination. Built for Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model (ADaM) workflows and regulatory submissions to agencies such as the Food and Drug Administration (FDA), European Medicines Agency (EMA), and Pharmaceuticals and Medical Devices Agency (PMDA).

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.

Measuring angles between points in a landscape is much easier than measuring distances. When the location of three points is known the position of the observer can be determined based solely on the angles between these points as seen by the observer. This task (known as triangulation) however requires onerous calculations - these calculations are automated by this package.

The German national forest inventory uses angle count sampling, a sampling method first published as `Bitterlich, W.: Die Winkelzählmessung. Allgemeine Forst- und Holzwirtschaftliche Zeitung, 58. Jahrg., Folge 11/12 vom Juni 1947` and extended by Grosenbaugh (<https://academic.oup.com/jof/article-abstract/50/1/32/4684174>) as probability proportional to size sampling. When plots are located near stand boundaries, their sizes and hence their probabilities need to be corrected.

Bayesian trophic position models using stan by leveraging brms for stable isotope data. Trophic position models are derived by using equations from Post (2002) <doi:10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2>, Vander Zanden and Vadeboncoeur (2002) <doi:10.1890/0012-9658(2002)083[2152:FAIOBA]2.0.CO;2>, and Heuvel et al. (2024) <doi:10.1139/cjfas-2024-0028>.

This package implements a task queue system for asynchronous parallel computing using PostgreSQL <https://www.postgresql.org/> as a backend. Designed for embarrassingly parallel problems where tasks do not communicate with each other. Dynamically distributes tasks to workers, handles uneven load balancing, and allows new workers to join at any time. Particularly useful for running large numbers of independent tasks on high-performance computing (HPC) clusters with SLURM <https://slurm.schedmd.com/> job schedulers.

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.

This package provides a teal_data class as a unified data model for teal applications focusing on reproducibility and relational data.

DEPRECATED: This package has been superseded by ThSQCA'. Please use install.packages('ThSQCA') instead. Provides threshold sweep methods for Qualitative Comparative Analysis (QCA). Implements Condition Threshold Sweep-Single (CTS-S), Condition Threshold Sweep-Multiple (CTS-M), Outcome Threshold Sweep (OTS), and Dual Threshold Sweep (DTS) for systematic exploration of threshold calibration effects on crisp-set QCA results. These methods extend traditional robustness approaches by treating threshold variation as an exploratory tool for discovering causal structures. Also provides Fiss (2011) <doi:10.5465/amj.2011.60263120> core/peripheral condition classification via compute_fiss_core() and generate_fiss_chart(), enabling four-symbol configuration charts that distinguish core conditions (present in both parsimonious and intermediate solutions) from peripheral conditions (intermediate only). Built on top of the QCA package by Dusa (2019) <doi:10.1007/978-3-319-75668-4>, with function arguments following QCA conventions. Based on set-theoretic methods by Ragin (2008) <doi:10.7208/chicago/9780226702797.001.0001> and established robustness protocols by Rubinson et al. (2019) <doi:10.1177/00491241211036158>.

This package provides a full-featured client for the Telegram MTProto protocol (<https://core.telegram.org/api>), enabling programmatic access to Telegram chats, channels, messages, media, and stories. Implements binary encoding and decoding of the Telegram TL (Type Language) schema, authentication (including two-factor), encrypted transport, and high-level helpers for downloading channel history and reactions at scale. Intended for social-science research and data collection tasks that require direct API access rather than the Bot API'.

Implementation of the tree-guided feature selection and logic aggregation approach introduced in Chen et al. (2024) <doi:10.1080/01621459.2024.2326621>. The method enables the selection and aggregation of large-scale rare binary features with a known hierarchical structure using a convex, linearly-constrained regularized regression framework. The package facilitates the application of this method to both linear regression and binary classification problems by solving the optimization problem via the smoothing proximal gradient descent algorithm (Chen et al. (2012) <doi:10.1214/11-AOAS514>).

Implementation of a Bayesian two-way latent structure model for integrative genomic clustering. The model clusters samples in relation to distinct data sources, with each subject-dataset receiving a latent cluster label, though cluster labels have across-dataset meaning because of the model formulation. A common scaling across data sources is unneeded, and inference is obtained by a Gibbs Sampler. The model can fit multivariate Gaussian distributed clusters or a heavier-tailed modification of a Gaussian density. Uniquely among integrative clustering models, the formulation makes no nestedness assumptions of samples across data sources -- the user can still fit the model if a study subject only has information from one data source. The package provides a variety of post-processing functions for model examination including ones for quantifying observed alignment of clusterings across genomic data sources. Run time is optimized so that analyses of datasets on the order of thousands of features on fewer than 5 datasets and hundreds of subjects can converge in 1 or 2 days on a single CPU. See "Swanson DM, Lien T, Bergholtz H, Sorlie T, Frigessi A, Investigating Coordinated Architectures Across Clusters in Integrative Studies: a Bayesian Two-Way Latent Structure Model, 2018, <doi:10.1101/387076>, Cold Spring Harbor Laboratory" at <https://www.biorxiv.org/content/early/2018/08/07/387076.full.pdf> for model details.

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.

Equilibrium models are frequently employed to examine the potential impacts of economic, energy, and trade policies as well as form the foundation of most integrated assessment models. The teems package handles all aspects of running equilibrium models while facilitating transparent and reproducible workflows.

Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series. Zekai, S. (2011). <doi:10.1061/(ASCE)HE.1943-5584.0000556>. Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp.

This package provides a convenient R interface to the National Health Service NHS Technology Reference Update Distribution (TRUD) API', allowing users to list available releases for their subscribed items, retrieve metadata, and download release files. For more information on the API, see <https://isd.digital.nhs.uk/trud/users/guest/filters/0/api>.

Write output (plots and tables) ensuring traceability back to code. Includes a graphics saver with simple automation of stamping with source, destination and creation time. A list of plots can be saved at once. A user-friendly selection of output dimensions for presentations, on-screen inspections, and more available.

Most estimators implemented by the video game industry cannot obtain reliable initial estimates nor guarantee comparability between distant estimates. TrueSkill Through Time solves all these problems by modeling the entire history of activities using a single Bayesian network allowing the information to propagate correctly throughout the system. This algorithm requires only a few iterations to converge, allowing millions of observations to be analyzed using any low-end computer. Landfried G, Mocskos E (2025). "TrueSkill Through Time: Reliable Initial Skill Estimates and Historical Comparability with Julia, Python, and R." <doi:10.18637/jss.v112.i06>. The core ideas implemented in this project were developed by Dangauthier P, Herbrich R, Minka T, Graepel T (2007). "Trueskill through time: Revisiting the history of chess.".