Implementation of the FVIBES, the Fuzzy Variable-Importance Based Eigenspace Separation algorithm as described in the paper by Ghashti, J.S., Hare, W., and J.R.J. Thompson (2025). Variable-Weighted Adjacency Constructions for Fuzzy Spectral Clustering. Submitted.

One can find single-stage and two-stage designs for a phase II single-arm study with either efficacy or safety/toxicity endpoints as described in Kim and Wong (2019) <doi:10.29220/CSAM.2019.26.2.163>.

Define and compute with generalized spherical distributions - multivariate probability laws that are specified by a star shaped contour (directional behavior) and a radial component. The methods are described in Nolan (2016) <doi:10.1186/s40488-016-0053-0>.

This package provides tools to measure the reliability of an Information Retrieval test collection. It allows users to estimate reliability using Generalizability Theory and map those estimates onto well-known indicators such as Kendall tau correlation or sensitivity.

This package contains basic tools for sample size estimation in studies of interobserver/interrater agreement (reliability). Includes functions for both the power-based and confidence interval-based methods, with binary or multinomial outcomes and two through six raters.

This package provides easy access for sentiment lexicons for those who want to do text analysis in Portuguese texts. As of now, two Portuguese lexicons are available: SentiLex-PT02 and OpLexicon (v2.1 and v3.0).

Allows management of Meetup groups via the <https:www.meetup.com/meetup_api/>. Provided are a set of functions that enable fetching information of joined meetups, attendance, and members. This package requires the use of an API key.

Perform sensitivity analysis on ordinary differential equation based models, including ad-hoc graphical analyses based on structured sequences of parameters as well as local sensitivity analysis. Functions are provided for creating inputs, simulating scenarios and plotting outputs.

This package provides tools for constructing detailed synthetic human populations from frequency tables. Add ages based on age groups and sex, create households, add students to education facilities, create employers, add employers to employees, and create interpersonal networks.

An enterprise-targeted scalable and UI-standardized shiny framework including a variety of developer convenience functions with the goal of both streamlining robust application development while assisting with creating a consistent user experience regardless of application or developer.

This package provides a mechanism for easily generating and organizing a collection of seeds from a single seed, which may be subsequently used to ensure reproducibility in processes/pipelines that utilize multiple random components (e.g., trial simulation).

The sparse vector field consensus (SparseVFC) algorithm (Ma et al., 2013 <doi:10.1016/j.patcog.2013.05.017>) for robust vector field learning. Largely translated from the Matlab functions in <https://github.com/jiayi-ma/VFC>.

The package is a part of the gDR suite. It helps to prepare raw drug response data for downstream processing. It mainly contains helper functions for importing/loading/validating dose-response data provided in different file formats.

This package provides a dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessibility for new Bioconductor users is hopefully increased.

This package contains tools for the organization, display, and analysis of the sorts of data frequently encountered in phonetics research and experimentation, including the easy creation of IPA vowel plots, and the creation and manipulation of WAVE audio files.

Compute the values of various parameters evaluating how similar two multidimensional datasets structures are in multidimensional space, as described in: Jouan-Rimbaud, D., Massart, D. L., Saby, C. A., Puel, C. (1998), <doi:10.1016/S0169-7439(98)00005-7>. The computed parameters evaluate three properties, namely, the direction of the data sets, the variance-covariance of the data points, and the location of the data sets centroids. The package contains workhorse function jrparams(), as well as two helper functions Mboxtest() and JRsMahaldist(), and four example data sets.

We implement linear regression when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked, based on D'Haultfoeuille, Gaillac, Maurel (2022) <doi:10.3386/w29953>. The package allows for common regressors observed in both datasets, and for various shape constraints on the effect of covariates on the outcome of interest. It also provides the tools to perform a test of point identification. See the associated vignette <https://github.com/cgaillac/RegCombin/blob/master/RegCombin_vignette.pdf> for theory and code examples.

We curated 147 of expression array, from 3 species(human,mouse,rat), 3 companies('Affymetrix','Illumina','Agilent'), by aligning the Fasta sequences of all probes of each platform to their corresponding reference genome, and then annotate them to genes.

This package provides functions to create and select graphical themes for the base plotting system. Contains: 1) several custom pre-made themes 2) mechanism for creating new themes by making persistent changes to the graphical parameters of base plots.

Visualizing the types and distribution of elements within bio-sequences. At the same time, We have developed a geom layer, geom_rrect(), that can generate rounded rectangles. No external references are used in the development of this package.

Create descriptive file names with ease. New file names are automatically (but optionally) time stamped and placed in date stamped directories. Streamline your analysis pipeline with input and output file names that have informative tags and proper file extensions.

Supports fMRI (functional magnetic resonance imaging) analysis tasks including reading in CIFTI', GIFTI and NIFTI data, temporal filtering, nuisance regression, and aCompCor (anatomical Components Correction) (Muschelli et al. (2014) <doi:10.1016/j.neuroimage.2014.03.028>).

This is a dataset package for GANPA, which implements a network-based gene weighting approach to pathway analysis. This package includes data useful for GANPA, such as a functional association network, pathways, an expression dataset and multi-subunit proteins.

This package provides a ggplot2 extension for visualizing vector fields in two-dimensional space. Provides flexible tools for creating vector and stream field layers, visualizing gradients and potential fields, and smoothing vector and scalar data to estimate underlying patterns.