Covers k-table control analysis using multivariate control charts for qualitative variables using fundamentals of multiple correspondence analysis and multiple factor analysis. The graphs can be shown in a flat or interactive way, in the same way all the outputs can be shown in an interactive shiny panel.

This package creates useful files and folders for data analysis projects and provides functions to manage data, scripts and output files. Also provides a project template for Rstudio'.

The goal of tosr is to create the Tree of Science from Web of Science (WoS) and Scopus data. It can read files from both sources at the same time. More information can be found in Valencia-Hernández (2020) <https://revistas.unal.edu.co/index.php/ingeinv/article/view/77718>.

Offers a TableContainer() function to create tables enriched with row, column, and table annotations. This package is similar to SummarizedExperiment in Bioconductor <doi:10.18129/B9.bioc.SummarizedExperiment>, but designed to work independently of Bioconductor, it ensures annotations are automatically updated when the table is subset. Additionally, it includes format_tbl() methods for enhanced table formatting and display.

Several datasets which describe the challenges and results of competitions in Tournament of Champions. This data is useful for practicing data wrangling, graphing, and analyzing how each season of Tournament of Champions played out.

This package provides the "r, q, p, and d" distribution functions for the triangle distribution. Also includes maximum likelihood estimation of parameters.

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.

Objects to manipulate sequential and seasonal time series. Sequential time series based on time instants and time duration are handled. Both can be regularly or unevenly spaced (overlapping duration are allowed). Only POSIX* format are used for dates and times. The following classes are provided : POSIXcti', POSIXctp', TimeIntervalDataFrame', TimeInstantDataFrame', SubtimeDataFrame ; methods to switch from a class to another and to modify the time support of series (hourly time series to daily time series for instance) are also defined. Tools provided can be used for instance to handle environmental monitoring data (not always produced on a regular time base).

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>.

Download and compile any version of the IANA Time Zone Database (also known as Olson database) and make it current in your R session. Beware: on Windows Cygwin is required!

This package provides a toolbox to assist with statistical analysis of animal trajectories. It provides simple access to algorithms for calculating and assessing a variety of characteristics such as speed and acceleration, as well as multiple measures of straightness or tortuosity. Some support is provided for 3-dimensional trajectories. McLean & Skowron Volponi (2018) <doi:10.1111/eth.12739>.

This package creates interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. treeheatr utilizes the customizable ggparty package for drawing decision trees.

This package provides a comprehensive toolset for any useR conducting topological data analysis, specifically via the calculation of persistent homology in a Vietoris-Rips complex. The tools this package currently provides can be conveniently split into three main sections: (1) calculating persistent homology; (2) conducting statistical inference on persistent homology calculations; (3) visualizing persistent homology and statistical inference. The published form of TDAstats can be found in Wadhwa et al. (2018) <doi:10.21105/joss.00860>. For a general background on computing persistent homology for topological data analysis, see Otter et al. (2017) <doi:10.1140/epjds/s13688-017-0109-5>. To learn more about how the permutation test is used for nonparametric statistical inference in topological data analysis, read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>. To learn more about how TDAstats calculates persistent homology, you can visit the GitHub repository for Ripser, the software that works behind the scenes at <https://github.com/Ripser/ripser>. This package has been published as Wadhwa et al. (2018) <doi:10.21105/joss.00860>.

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.

An efficient algorithm for data twinning. This work is supported by U.S. National Science Foundation grants DMREF-1921873 and CMMI-1921646.

We described a novel Topology-based pathway enrichment analysis, which integrated the global position of the nodes and the topological property of the pathways in Kyoto Encyclopedia of Genes and Genomes Database. We also provide some functions to obtain the latest information about pathways to finish pathway enrichment analysis using this method.

Analyzing treatment effects in clinical trials with time-to-event outcomes is complicated by intercurrent events. This package implements methods for estimating and inferring the cumulative incidence functions for time-to-event (TTE) outcomes with intercurrent events (ICEs) under the five strategies outlined in the ICH E9 (R1) addendum, see Deng (2025)<doi:10.1002/sim.70091>. This package can be used for analyzing data from both randomized controlled trials and observational studies. In general, we have a primary outcome event and possibly an intercurrent event. Two data structures are allowed: competing risks, where only the time to the first event is recorded, and semicompeting risks, where the times to both the primary outcome event and intercurrent event (or censoring) are recorded. For estimation methods, users can choose nonparametric estimation (which does not use covariates) and semiparametrically efficient estimation.

The satisfaction Analysis using the tetraclasse model from Sylvie Llosa. Llosa (1997) <http://www.jstor.org/stable/40592578>.

This package provides a flexible simulation tool for phylogenetic trees under a general model for speciation and extinction. Trees with a user-specified number of extant tips, or a user-specified stem age are simulated. It is possible to assume any probability distribution for the waiting time until speciation and extinction. Furthermore, the waiting times to speciation / extinction may be scaled in different parts of the tree, meaning we can simulate trees with clade-dependent diversification processes. At a speciation event, one species splits into two. We allow for two different modes at these splits: (i) symmetric, where for every speciation event new waiting times until speciation and extinction are drawn for both daughter lineages; and (ii) asymmetric, where a speciation event results in one species with new waiting times, and another that carries the extinction time and age of its ancestor. The symmetric mode can be seen as an vicariant or allopatric process where divided populations suffer equal evolutionary forces while the asymmetric mode could be seen as a peripatric speciation where a mother lineage continues to exist. Reference: O. Hagen and T. Stadler (2017). TreeSimGM: Simulating phylogenetic trees under general Bellman Harris models with lineage-specific shifts of speciation and extinction in R. Methods in Ecology and Evolution. <doi:10.1111/2041-210X.12917>.

This package provides a tidy set of functions for summarising data, including descriptive statistics, frequency tables with normality testing, and group-wise significance testing. Designed for fast, readable, and easy exploration of both numeric and categorical data.

This contains functions that can be used to estimate the time-dependent precision-recall curve (PRC) and the corresponding area under the PRC for right-censored survival data. It also compute time-dependent ROC curve and its corresponding area under the ROC curve (AUC). See Beyene, Chen and Kifle (2024) <doi:10.1002/bimj.202300135>.

Streamline the process of accessing fundamental financial data from the United States Securities and Exchange Commission's ('SEC') Electronic Data Gathering, Analysis, and Retrieval system ('EDGAR') API <https://www.sec.gov/edgar/sec-api-documentation>, transforming it into a tidy, analysis-ready format.

Defines S3 vector data types for vectors of functional data (grid-based, spline-based or functional principal components-based) with all arithmetic and summary methods, derivation, integration and smoothing, plotting, data import and export, and data wrangling, such as re-evaluating, subsetting, sub-assigning, zooming into sub-domains, or extracting functional features like minima/maxima and their locations. The implementation allows including such vectors in data frames for joint analysis of functional and scalar variables.

This package provides a coherent interface for evaluating models fit with the trending package. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.