Doubly robust estimation for the mean of an arbitrarily transformed survival time under covariate-induced dependent left truncation and noninformative right censoring. The functions truncAIPW(), truncAIPW_cen1(), and truncAIPW_cen2() compute the doubly robust estimators under the scenario without censoring and the two censoring scenarios, respectively. The package also contains three simulated data sets simu', simu_c1', and simu_c2', which are used to illustrate the usage of the functions in this package. Reference: Wang, Y., Ying, A., Xu, R. (2022) "Doubly robust estimation under covariate-induced dependent left truncation" <arXiv:2208.06836>.

Greedy optimal subset selection for transformation models (Hothorn et al., 2018, <doi:10.1111/sjos.12291> ) based on the abess algorithm (Zhu et al., 2020, <doi:10.1073/pnas.2014241117> ). Applicable to models from packages tram and cotram'. Application to shift-scale transformation models are described in Siegfried et al. (2024, <doi:10.1080/00031305.2023.2203177>).

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>.

This package provides classes and methods for trajectory data, with support for nesting individual Track objects in track sets (Tracks) and track sets for different entities in collections of Tracks. Methods include selection, generalization, aggregation, intersection, simulation, and plotting.

This package provides functions to combine data.frames in ways that require additional effort in base R, and to add metadata (id, title, ...) that can be used for printing and xlsx export. The Tatoo_report class is provided as a convenient helper to write several such tables to a workbook, one table per worksheet. Tatoo is built on top of openxlsx', but intimate knowledge of that package is not required to use tatoo.

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.

This package provides utilities to create and use lenses to simplify data manipulation. Lenses are composable getter/setter pairs that provide a functional approach to manipulating deeply nested data structures, e.g., elements within list columns in data frames. The implementation is based on the earlier lenses R package <https://github.com/cfhammill/lenses>, which was inspired by the Haskell lens package by Kmett (2012) <https://github.com/ekmett/lens>, one of the most widely referenced implementations of lenses. For additional background and history on the theory of lenses, see the lens package wiki: <https://github.com/ekmett/lens/wiki/History-of-Lenses>.

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.

Calculates total survey error (TSE) for one or more surveys, using common scale-dependent and/or scale-independent metrics. On TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>.

Data collected on movement behavior is often in the form of time- stamped latitude/longitude coordinates sampled from the underlying movement behavior. These data can be compressed into a set of segments via the Top- Down Time Ratio Segmentation method described in Meratnia and de By (2004) <doi:10.1007/978-3-540-24741-8_44> which, with some loss of information, can both reduce the size of the data as well as provide corrective smoothing mechanisms to help reduce the impact of measurement error. This is an improvement on the well-known Douglas-Peucker algorithm for segmentation that operates not on the basis of perpendicular distances. Top-Down Time Ratio segmentation allows for disparate sampling time intervals by calculating the distance between locations and segments with respect to time. Provided a trajectory with timestamps, tdtr() returns a set of straight- line segments that can represent the full trajectory. McCool, Lugtig, and Schouten (2022) <doi:10.1007/s11116-022-10328-2> describe this method as implemented here in more detail.

This package provides an interactive interface to the tfrmt package. Users can import, modify, and export tables and templates with little to no code.

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

This package provides functionality of a statistical testing implementation whether a dataset comes from a symmetric distribution when the center of symmetry is unknown, including Wilcoxon test and sign test procedure. In addition, sample size determination for both tests is provided. The Wilcoxon test procedure is described in Vexler et al. (2023) <https://www.sciencedirect.com/science/article/abs/pii/S0167947323000579>, and the sign test is outlined in Gastwirth (1971) <https://www.jstor.org/stable/2284233>.

Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of heterogeneous sources into a three-dimensional matrix of signals (features by observations by sources). The TCA framework further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects; particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a three-dimensional tensor of cell-type-specific methylation levels for each individual (i.e. methylation sites by individuals by cell types) and it allows to detect cell-type-specific statistical relations (associations) with phenotypes. For more details see Rahmani et al. (2019) <DOI:10.1038/s41467-019-11052-9>.

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.

Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <DOI:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.

Read General Transit Feed Specification (GTFS) zipfiles into a list of R dataframes. Perform validation of the data structure against the specification. Analyze the headways and frequencies at routes and stops. Create maps and perform spatial analysis on the routes and stops. Please see the GTFS documentation here for more detail: <https://gtfs.org/>.

Download daily interest rates from the US Treasury XML feed. Leveraging <https://home.treasury.gov/treasury-daily-interest-rate-xml-feed>, this package serves as a wrapper, facilitating the retrieval of daily treasury rates across various categories, including par yield curves, treasury bills, long-term rates, and real yield curves.

Efficient implementations of functions for the creation, modification and analysis of phylogenetic trees. Applications include: generation of trees with specified shapes; tree rearrangement; analysis of tree shape; rooting of trees and extraction of subtrees; calculation and depiction of split support; plotting the position of rogue taxa (Klopfstein & Spasojevic 2019) <doi:10.1371/journal.pone.0212942>; calculation of ancestor-descendant relationships, of stemwardness (Asher & Smith, 2022) <doi:10.1093/sysbio/syab072>, and of tree balance (Mir et al. 2013, Lemant et al. 2022) <doi:10.1016/j.mbs.2012.10.005>, <doi:10.1093/sysbio/syac027>; artificial extinction (Asher & Smith, 2022) <doi:10.1093/sysbio/syab072>; import and export of trees from Newick, Nexus (Maddison et al. 1997) <doi:10.1093/sysbio/46.4.590>, and TNT <https://www.lillo.org.ar/phylogeny/tnt/> formats; and analysis of splits and cladistic information.

STARMA (Space-Time Autoregressive Moving Average) models are commonly utilized in modeling and forecasting spatiotemporal time series data. However, the intricate nonlinear dynamics observed in many space-time rainfall patterns often exceed the capabilities of conventional STARMA models. This R package enables the fitting of Time Delay Spatio-Temporal Neural Networks, which are adept at handling such complex nonlinear dynamics efficiently. For detailed methodology, please refer to Saha et al. (2020) <doi:10.1007/s00704-020-03374-2>.

Handling taxonomic lists through objects of class taxlist'. This package provides functions to import species lists from Turboveg (<https://www.synbiosys.alterra.nl/turboveg/>) and the possibility to create backups from resulting R-objects. Also quick displays are implemented as summary-methods.

Produce an HTML page containing horizontal strips that symbolize events in a person's lsife. Since this is entirely a visualization, the image <https://barryzee.github.io/henry-timeline/henry.html> will show the basic use to show a timeline of events. The image <https://barryzee.github.io/vermeer/cssOverlay.html> shows how to correlate two timelines of events. A brief description is available at <https://barryzee.github.io/timeLineGraphics_manuscript/golden_age.html>.

Delta Method implementation to estimate standard errors with known asymptotic properties within the tidyverse workflow. The Delta Method is a statistical tool that approximates an estimatorâ s behaviour using a Taylor Expansion. For a comprehensive explanation, please refer to Chapter 3 of van der Vaart (1998, ISBN: 9780511802256).

The main objective of cooperative Transferable-Utility games (TU-games) is to allocate a good among the agents involved. The package implements major solution concepts including the Shapley value, Banzhaf value, and egalitarian rules, alongside their extensions for structured games: the Owen value and Banzhaf-Owen value for games with a priori unions, and the Myerson value for communication games on networks. To address the inherent exponential computational complexity of exact evaluation, the package offers both exact algorithms and linear approximation methods based on sampling, enabling the analysis of large-scale games. Additionally, it supports core set-based solutions, allowing computation of the vertices and the centroid of the core.