The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as GoMiner (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. The significance is traditionally determined by randomizing the input gene list to computing the false discovery rate (FDR) of the enrichment p-value for each category. We explore here the novel alternative of randomizing the GO database rather than the gene list.

Formats for R Markdown that undo modifications by pandoc and rmarkdown to original latex templates, such as smaller margins, paragraph spacing, and compact titles. In addition, enhancements such as author blocks with affiliations and headers and footers are introduced. All of this functionality is built around plugins that modify the default pandoc template without relying on custom templates.

This package provides a collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors. Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE (2013) <doi:10.1002/sim.5902>.

This package performs multinomial goodness-of-fit test on multinomially distributed data using the Randomized phi-divergence test statistics. Details of this kind of statistics can be found at Nikita Puchkin, Vladimir Ulyanov (2023) <doi:10.1214/22-AIHP1299>.

Use R to interface with the TD Ameritrade API <https://developer.tdameritrade.com/>. Functions include authentication, trading, price requests, account information, and option chains. A user will need a TD brokerage account and TD Ameritrade developer app. See README for authentication process and examples.

This package implements the rquery piped Codd-style query algebra using data.table'. This allows for a high-speed in memory implementation of Codd-style data manipulation tools.

Researchers commonly need to summarize scientific information, a process known as evidence synthesis'. The first stage of a synthesis process (such as a systematic review or meta-analysis) is to download a list of references from academic search engines such as Web of Knowledge or Scopus'. The traditional approach to systematic review is then to sort these data manually, first by locating and removing duplicated entries, and then screening to remove irrelevant content by viewing titles and abstracts (in that order). revtools provides interfaces for each of these tasks. An alternative approach, however, is to draw on tools from machine learning to visualise patterns in the corpus. In this case, you can use revtools to render ordinations of text drawn from article titles, keywords and abstracts, and interactively select or exclude individual references, words or topics.

Visualizations to explain the results of a topological data analysis. The goal of topological data analysis is to identify persistent topological structures, such as loops (topological circles) and voids (topological spheres), in data sets. The output of an analysis using the TDA package is a Rips diagram (named after the mathematician Eliyahu Rips). The goal of RPointCloud is to fill in these holes in the data by providing tools to visualize the features that help explain the structures found in the Rips diagram. See McGee and colleagues (2024) <doi:10.1101/2024.05.16.593927>.

This package provides randomization tests and graphical diagnostics for assessing randomized assignment and covariate balance for a binary treatment variable. See Branson (2021) <arXiv:1804.08760> for details.

Open any data frame with visidata', a terminal-based spreadsheet application <https://www.visidata.org>.

Risk-related information (like the prevalence of conditions, the sensitivity and specificity of diagnostic tests, or the effectiveness of interventions or treatments) can be expressed in terms of frequencies or probabilities. By providing a toolbox of corresponding metrics and representations, riskyr computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent (see <doi:10.3389/fpsyg.2020.567817>, for details).

This package provides a collection of tools for extracting structured data from <https://www.reddit.com/>.

NanoString nCounter is a medium-throughput platform that measures gene or microRNA expression levels. Here is a publication that introduces this platform: Malkov (2009) <doi:10.1186/1756-0500-2-80>. Here is the webpage of NanoString nCounter where you can find detailed information about this platform <https://www.nanostring.com/scientific-content/technology-overview/ncounter-technology>. It has great clinical application, such as diagnosis and prognosis of cancer. Implements integrated system of random-coefficient hierarchical regression model to normalize data from NanoString nCounter platform so that noise from various sources can be removed.

Programmatic interface to the Web Service methods provided by the National Phenology Network (<https://usanpn.org/>), which includes data on various life history events that occur at specific times.

This package contains inferential and graphical routines for comparing two treatment arms in terms of the restricted mean time in favor of treatment.

Designed to streamline data analysis and statistical testing, reducing the length of R scripts while generating well-formatted outputs in pdf', Microsoft Word', and Microsoft Excel formats. In essence, the package contains functions which are sophisticated wrappers around existing R functions that are called by using f_ (user f_riendly) prefix followed by the normal function name. This first version of the rfriend package focuses primarily on data exploration, including tools for creating summary tables, f_summary(), performing data transformations, f_boxcox() in part based on MASS/boxcox and rcompanion', and f_bestNormalize() which wraps and extends functionality from the bestNormalize package. Furthermore, rfriend can automatically (or on request) generate visualizations such as boxplots, f_boxplot(), QQ-plots, f_qqnorm(), histograms f_hist(), and density plots. Additionally, the package includes four statistical test functions: f_aov(), f_kruskal_test(), f_glm(), f_chisq_test for sequential testing and visualisation of the stats functions: aov(), kruskal.test(), glm() and chisq.test. These functions support testing multiple response variables and predictors, while also handling assumption checks, data transformations, and post hoc tests. Post hoc results are automatically summarized in a table using the compact letter display (cld) format for easy interpretation. The package also provides a function to do model comparison, f_model_comparison(), and several utility functions to simplify common R tasks. For example, f_clear() clears the workspace and restarts R with a single command; f_setwd() sets the working directory to match the directory of the current script; f_theme() quickly changes RStudio themes; and f_factors() converts multiple columns of a data frame to factors, and much more. If you encounter any issues or have feature requests, please feel free to contact me via email.

Suite of tools for using D3', a library for producing dynamic, interactive data visualizations. Supports translating objects into D3 friendly data structures, rendering D3 scripts, publishing D3 visualizations, incorporating D3 in R Markdown, creating interactive D3 applications with Shiny, and distributing D3 based htmlwidgets in R packages.

We provide functions to perform taxometric analyses. This package contains 46 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method.

The goal of Rigma is to provide a user friendly client to the Figma API <https://www.figma.com/developers/api>. It uses the latest `httr2` for a stable interface with the REST API. More than 20 methods are provided to interact with Figma files, and teams. Get design data into R by reading published components and styles, converting and downloading images, getting access to the full Figma file as a hierarchical data structure, and much more. Enhance your creativity and streamline the application development by automating the extraction, transformation, and loading of design data to your applications and HTML documents.

An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>, a C library of highly efficient parallelizable functions for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). Due to the possible portability issues CRAN version no longer supports OpenMP, but you can install OpenMP-supported version from GitHub: <https://github.com/helske/Rlibeemd/>.

Pretty fast implementation of the Ramer-Douglas-Peucker algorithm for reducing the number of points on a 2D curve. Urs Ramer (1972), "An iterative procedure for the polygonal approximation of plane curves" <doi:10.1016/S0146-664X(72)80017-0>. David H. Douglas and Thomas K. Peucker (1973), "Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature" <doi:10.3138/FM57-6770-U75U-7727>.

The aim of this package is to manipulate relational data models in R. It provides functions to create, modify and export data models in json format. It also allows importing models created with MySQL Workbench (<https://www.mysql.com/products/workbench/>). These functions are accessible through a graphical user interface made with shiny'. Constraints such as types, keys, uniqueness and mandatory fields are automatically checked and corrected when editing a model. Finally, real data can be confronted to a model to check their compatibility.

Ray Shooting Depth functions are provided for bivariate analysis. This mainly includes functions for computing the bivariate depth as well as RS median. Drawing functions for depth bags are also provided.

An intuitive and explainable metric of Feature Importance for Classification Problems. Resolution Index measures the extent to which a Feature clusters different classes when data is sorted on it. User provides a DataFrame, column name of the Class, sample size and number of iterations used for calculation. Resolution Index for each Feature is returned, which can be effectively used to rank Features and reduce Dimensionality of Training data. For more details on Feature Selection see Theng and Bhoyar (2023) <doi:10.1007/s10115-023-02010-5>.