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. In addition, it provides access to the monthly published yield curve datasets, including the High Quality Market (HQM) corporate bond yield curve and the Treasury nominal and real coupon-issue (TNC, TRC) and breakeven inflation (TBI) curves.

This package provides users a quick exploratory dive into common visualizations without writing a single line of code given the users data follows the Analysis Data Model (ADaM) standards put forth by the Clinical Data Interchange Standards Consortium (CDISC) <https://www.cdisc.org>. Prominent modules/ features of the application are the Table Generator, Population Explorer, and the Individual Explorer. The Table Generator allows users to drag and drop variables and desired statistics (frequencies, means, ANOVA, t-test, and other summary statistics) into bins that automagically create stunning tables with validated information. The Population Explorer offers various plots to visualize general trends in the population from various vantage points. Plot modules currently include scatter plot, spaghetti plot, box plot, histogram, means plot, and bar plot. Each plot type allows the user to plot uploaded variables against one another, and dissect the population by filtering out certain subjects. Last, the Individual Explorer establishes a cohesive patient narrative, allowing the user to interact with patient metrics (params) by visit or plotting important patient events on a timeline. All modules allow for concise filtering & downloading bulk outputs into html or pdf formats to save for later.

Handling and manipulation polygons, coordinates, and other geographical objects. The tools include: polygon areas, barycentric and trilinear coordinates (Hormann and Floater, 2006, <doi:10.1145/1183287.1183295>), convex hull for polygons (Graham and Yao, 1983, <doi:10.1016/0196-6774(83)90013-5>), polygon triangulation (Toussaint, 1991, <doi:10.1007/BF01905693>), great circle and geodesic distances, Hausdorff distance, and reduced major axis.

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.

This package provides tools for working with temporal discounting data, designed for behavioural researchers to simplify data cleaning/scoring and model fitting. The package implements widely used methods such as computing indifference points from adjusting amount task (Frye et al., 2016, <doi:10.3791/53584>), testing for non-systematic discounting per the criteria of Johnson & Bickel (2008, <doi:10.1037/1064-1297.16.3.264>), scoring questionnaires according to the methods of Kirby et al. (1999, <doi:10.1037//0096-3445.128.1.78>) and Wileyto et al (2004, <doi:10.3758/BF03195548>), Bayesian model selection using a range of discount functions (Franck et al., 2015, <doi:10.1002/jeab.128>), drift diffusion models of discounting (Peters & D'Esposito, 2020, <doi:10.1371/journal.pcbi.1007615>), and model-agnostic measures of discounting such as area under the curve (Myerson et al., 2001, <doi:10.1901/jeab.2001.76-235>) and ED50 (Yoon & Higgins, 2008, <doi:10.1016/j.drugalcdep.2007.12.011>).

This package provides a diverse collection of time series datasets spanning various fields such as economics, finance, energy, healthcare, and more. Designed to support time series analysis in R by offering datasets from multiple disciplines, making it a valuable resource for researchers and analysts.

Graphic interface for text analysis, implement a few methods such as biplots, correspondence analysis, co-occurrence, clustering, topic models, correlations and sentiments.

This package contains summary data on gene expression in normal human tissues from the Human Protein Atlas for use with the Tissue-Adjusted Pathway Analysis of cancer (TPAC) method. Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.

This queue is a data structure that lets parallel processes send and receive messages, and it can help coordinate the work of complicated parallel tasks. Processes can push new messages to the queue, pop old messages, and obtain a log of all the messages ever pushed. File locking preserves the integrity of the data even when multiple processes access the queue simultaneously.

Third order response surface designs (M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI:10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts") are classified into two types viz., designs which are suitable for sequential experimentation and designs for non-sequential experimentation (M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2022)<DOI:10.1080/02664763.2020.1864817>." Sequential asymmetric third order rotatable designs (SATORDs)"). The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go.This package contains functions named STORDs() and NSTORDs() for generating sequential/non-sequential TORDs given in Das, M. N., and V. L. Narasimham (1962). <DOI:10.1214/aoms/1177704374>. "Construction of rotatable designs through balanced incomplete block designs" along with the randomized layout. It also contains another function named Pred3.var() for generating the variance of predicted response as well as the moment matrix based on a third order response surface model.

This package provides a shared tsibble data easily communicates between htmlwidgets on both client and server sides, powered by crosstalk'. A shiny module is provided to visually explore periodic/aperiodic temporal patterns.

This package provides a deterministic, dependency-free documentation generator for R packages. Generates valid Rd files and NAMESPACE from roxygen2'-style comments using only base R. Supports a strict subset of tags with no markdown parsing, no inference magic, and explicit-only behavior.

Interacts with a suite of web application programming interfaces (API) for taxonomic tasks, such as getting database specific taxonomic identifiers, verifying species names, getting taxonomic hierarchies, fetching downstream and upstream taxonomic names, getting taxonomic synonyms, converting scientific to common names and vice versa, and more. Some of the services supported include NCBI E-utilities (<https://www.ncbi.nlm.nih.gov/books/NBK25501/>), Encyclopedia of Life (<https://eol.org/docs/what-is-eol/data-services>), Global Biodiversity Information Facility (<https://techdocs.gbif.org/en/openapi/>), and many more. Links to the API documentation for other supported services are available in the documentation for their respective functions in this package.

Enables users to build ToxPi prioritization models and provides functionality within the grid framework for plotting ToxPi graphs. toxpiR allows for more customization than the ToxPi GUI (<https://toxpi.github.io/>) and integration into existing workflows for greater ease-of-use, reproducibility, and transparency. toxpiR package behaves nearly identically to the GUI; the package documentation includes notes about all differences. The vignettes download example files from <https://github.com/ToxPi/ToxPi-example-files>.

Perform test to detect differences in structure between families of trees. The method is based on cophenetic distances and aggregated Student's tests.

Interface to TensorFlow Datasets, a high-level library for building complex input pipelines from simple, re-usable pieces. See <https://www.tensorflow.org/guide> for additional details.

This package provides conditional maximum likelihood (CML) item parameter estimation of both sequential and cumulative deterministic multistage designs (Zwitser & Maris, 2015, <doi:10.1007/s11336-013-9369-6>) and probabilistic sequential and cumulative multistage designs (Steinfeld & Robitzsch, 2024, <doi:10.1007/s41237-024-00228-3>). Supports CML item parameter estimation of conventional linear designs and additional functions for the likelihood ratio test (Andersen, 1973, <doi:10.1007/BF02291180>) as well as functions for simulating various types of multistage designs.

This package provides a suite of functions for analysing, interpreting, and visualising time-series features calculated from different feature sets from the theft package. Implements statistical learning methodologies described in Henderson, T., Bryant, A., and Fulcher, B. (2023) <doi:10.48550/arXiv.2303.17809>.

Split a dataframe, tibble, or data.table into training and test sets. Return either a list, an index, or directly assign training and test sets into memory.

With the objective of including data from RSS feeds into your analysis, tidyRSS parses RSS, Atom and JSON feeds and returns a tidy data frame.

The R language includes a set of defined types, but the language itself is "absurdly dynamic" (Turcotte & Vitek (2019) <doi:10.1145/3340670.3342426>), and lacks any way to specify which types are expected by any expression. The typetracer package enables code to be traced to extract detailed information on the properties of parameters passed to R functions. typetracer can trace individual functions or entire packages.

This package provides a compilation of fish stock assessment methods for the analysis of length-frequency data in the context of data-poor fisheries. Includes methods and examples included in the FAO Manual by P. Sparre and S.C. Venema (1998), "Introduction to tropical fish stock assessment" (<https://openknowledge.fao.org/server/api/core/bitstreams/bc7c37b6-30df-49c0-b5b4-8367a872c97e/content>), as well as other more recent methods.

Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) <doi:10.1111/2041-210X.14038>.

This package performs maximum likelihood based estimation and inference on time to event data, possibly subject to non-informative right censoring. FitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. CompParaSurv() compares the mean, median, and restricted mean survival experiences of two treatment groups. Candidate distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull.