This package provides a framework for text cleansing and analysis. Conveniently prepare and process large amounts of text for analysis. Includes various metrics for word counts/frequencies that scale efficiently. Quickly analyze large amounts of text data using a text.table (a data.table created with one word (or unit of text analysis) per row, similar to the tidytext format). Offers flexibility to efficiently work with text data stored in vectors as well as text data formatted as a text.table.

Carries out analyses of two-way tables with one observation per cell, together with graphical displays for an additive fit and a diagnostic plot for removable non-additivity via a power transformation of the response. It implements Tukey's Exploratory Data Analysis (1973) <ISBN: 978-0201076165> methods, including a 1-degree-of-freedom test for row*column non-additivity', linear in the row and column effects.

The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, the relationship between an unmeasured confounder and the outcome, for example a plausible residual effect size for an unmeasured continuous or binary confounder, and the relationship between an unmeasured confounder and the exposure, for example a realistic mean difference or prevalence difference for this hypothetical confounder between exposure groups. Building on the methods put forth by Cornfield et al. (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983), Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli & Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele (2016), we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance.

This package provides functions for implementing the targeted gold standard (GS) testing. You provide the true disease or treatment failure status and the risk score, tell TGST the availability of GS tests and which method to use, and it returns the optimal tripartite rules. Please refer to Liu et al. (2013) <doi:10.1080/01621459.2013.810149> for more details.

Computes treatment patterns within a given cohort using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). As described in Markus, Verhamme, Kors, and Rijnbeek (2022) <doi:10.1016/j.cmpb.2022.107081>.

Two-stage procedure compares hazard rate functions, which may or may not cross each other.

Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees.

Consolidates and calculates different sets of time-series features from multiple R and Python packages including Rcatch22 Henderson, T. (2021) <doi:10.5281/zenodo.5546815>, feasts O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>, tsfeatures Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020) <https://CRAN.R-project.org/package=tsfeatures>, tsfresh Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, TSFEL Barandas, M., et al. (2020) <doi:10.1016/j.softx.2020.100456>, and Kats Facebook Infrastructure Data Science (2021) <https://facebookresearch.github.io/Kats/>.

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.

Constraint-based causal discovery using the PC algorithm while accounting for a partial node ordering, for example a partial temporal ordering when the data were collected in different waves of a cohort study. Andrews RM, Foraita R, Didelez V, Witte J (2021) <arXiv:2108.13395> provide a guide how to use tpc to analyse cohort data.

This package provides access to YouTube Analytics API v2 for retrieving YouTube Analytics data including views, engagement metrics, demographics, and revenue data. Supports OAuth 2.0 authentication and channel group management. See <https://developers.google.com/youtube/analytics/> for API documentation.

Tree Ring Analysis of Disturbance Events in R (TRADER) package provides functions for disturbance reconstruction from tree-ring data, e.g. boundary line, absolute increase, growth averaging methods.

Compute arbitrary non-parametric bootstrap statistics on data in tidy data frames.

This package implements the Topic Testlet Model (TTM) as described by Xiong et al. (2025) <doi:10.1111/jedm.70001>. The package integrates Latent Dirichlet Allocation (LDA) with the Partial Credit Model to account for local item dependence in testlets using latent topics from student textual responses.

This package provides utility functions for data analysis and computing. Includes functions for logging, parallel processing, and other computational tasks to streamline workflows.

Extension of funHDDC Schmutz et al. (2018) <doi:10.1007/s00180-020-00958-4> for cases including outliers by fitting t-distributions for robust groups. TFunHDDC can cluster univariate or multivariate data produced by the fda package for data using a b-splines or Fourier basis.

Convert semi-structured log files (such as Apache access.log files) into a tabular format (data.frame) using a standard template system.

This package provides a general framework of two directional simultaneous inference is provided for high-dimensional as well as the fixed dimensional models with manifest variable or latent variable structure, such as high-dimensional mean models, high- dimensional sparse regression models, and high-dimensional latent factors models. It is making the simultaneous inference on a set of parameters from two directions, one is testing whether the estimated zero parameters indeed are zero and the other is testing whether there exists zero in the parameter set of non-zero. More details can be referred to Wei Liu, et al. (2022) <doi:10.48550/arXiv.2012.11100>.

This package creates some WebGL shaders. They can be used as the background of a Shiny app. They also can be visualized in the RStudio viewer pane or included in Rmd documents, but this is pretty useless, besides contemplating them.

This package provides a bioinformatics tool for the estimation of the tumor purity from sequencing data. It uses the set of putative clonal somatic single nucleotide variants within copy number neutral segments to call tumor cellularity.

Tabu search algorithm for binary configurations. A basic version of the algorithm as described by Fouskakis and Draper (2007) <doi:10.1111/j.1751-5823.2002.tb00174.x>.

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 a reproducible pipeline for deriving two clinically meaningful cutoffs for disease biomarkers using a unified two-stage framework. The package integrates finite mixture modeling with risk prediction using biomarker plus clinical features, followed by decision curve analysis to evaluate clinical utility. Outputs include biomarker density plots, risk calibration curves, decision curves, and summary tables of diagnostic performance. Designed for researchers in bio-statistics, neurology, and data science, this package emphasizes reproducibility, transparency, and clear clinical relevance.

Estimation of the SF-ACE, a Causal Inference estimand proposed in the paper "The Subtype-Free Average Causal Effect For Heterogeneous Disease Etiology" (soon on arXiv).