This package implements the quantile-graph based Dynamics Similarity Coefficient (DSC) for comparing intrinsic dynamics of time series.

The debar sequence processing pipeline is designed for denoising high throughput sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). The package is designed to detect and correct insertion and deletion errors within sequencer outputs. This is accomplished through comparison of input sequences against a profile hidden Markov model (PHMM) using the Viterbi algorithm (for algorithm details see Durbin et al. 1998, ISBN: 9780521629713). Inserted base pairs are removed and deleted base pairs are accounted for through the introduction of a placeholder character. Since the PHMM is a probabilistic representation of the COI barcode, corrections are not always perfect. For this reason debar censors base pairs adjacent to reported indel sites, turning them into placeholder characters (default is 7 base pairs in either direction, this feature can be disabled). Testing has shown that this censorship results in the correct sequence length being restored, and erroneous base pairs being masked the vast majority of the time (>95%).

This package provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.

In order to provide unified access to Linux distribution details in R, this package wraps the various files and commands that may exist on a system. It is similar in spirit to the lsb_release command and the Python package of the same name.

Dataset containing information about job listings for data science job roles.

This package provides a graphical user interface (GUI) to the functions implemented in the R package DQAstats'. Publication: Mang et al. (2021) <doi:10.1186/s12911-022-01961-z>.

Implementation of the DISCO (Density-based Internal Score for Clusterings with nOise) metric, a cluster validity index for evaluating density-based clustering results without ground truth labels. DISCO is the first index to explicitly assess the quality of noise point assignments in addition to cluster quality. It uses density-connectivity distance derived from a minimum spanning tree of the mutual-reachability graph, providing interpretable, bounded scores in [-1, 1]. Higher scores indicate better clustering. Based on Beer et al. (2025) <doi:10.48550/arXiv.2503.00127>.

This package creates interactive genome browser. It joins the data analysis power of R and the visualization libraries of JavaScript in one package. Barrios, D. & Prieto, C. (2017) <doi:10.1089/cmb.2016.0213>.

This package provides benchmark datasets and foundational tools for Symbolic Data Analysis (SDA). The package includes functions for constructing symbolic data objects from classical data, converting among different interval-valued data formats, managing interval-valued, histogram-valued, modal-valued, and multi-valued data, and performing basic descriptive statistics. It is designed to support teaching, methodological research, and the development of SDA techniques.

Finds regular and chaotic intervals in the data using the 0-1 test for chaos proposed by Gottwald and Melbourne (2004) <DOI:10.1137/080718851>.

This package provides a unified framework to building Area Deprivation Index (ADI), Social Vulnerability Index (SVI), and Neighborhood Deprivation Index (NDI) deprivation measures and accessing related data from the U.S. Census Bureau such as Gini coefficient data. Tools are also available for calculating percentiles, quantiles, and for creating clear map breaks for data visualization.

Compute degree days from daily min and max temperatures for modeling plant and insect development.

Spatial analyses involving binning require that every bin have the same area, but this is impossible using a rectangular grid laid over the Earth or over any projection of the Earth. Discrete global grids use hexagons, triangles, and diamonds to overcome this issue, overlaying the Earth with equally-sized bins. This package provides utilities for working with discrete global grids, along with utilities to aid in plotting such data.

Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed without making any parametric assumptions on the stable (in-control) distribution. See G. Capizzi and G. Masarotto (2018) <doi:10.1007/978-3-319-75295-2_1> for an introduction to the package.

The goal of dynamicpv is to provide a simple way to calculate (net) present values and outputs from health economic models (especially cost-effectiveness and budget impact) in discrete time that reflect dynamic pricing and dynamic uptake. Dynamic pricing is also known as life cycle pricing; dynamic uptake is also known as multiple or stacked cohorts, or dynamic disease prevalence. Shafrin (2024) <doi:10.1515/fhep-2024-0014> provides an explanation of dynamic value elements, in the context of Generalized Cost Effectiveness Analysis, and Puls (2024) <doi:10.1016/j.jval.2024.03.006> reviews challenges of incorporating such dynamic value elements. This package aims to reduce those challenges.

Helper functions for descriptive tasks such as making print-friendly bivariate tables, sample size flow counts, and visualizing sample distributions. Also contains R approximations of some common SAS and Stata functions such as PROC MEANS from SAS and ladder', gladder', and pwcorr from Stata'.

Model-based methods for the detection of disease clusters using GLMs, GLMMs and zero-inflated models. These methods are described in V. Gómez-Rubio et al. (2019) <doi:10.18637/jss.v090.i14> and V. Gómez-Rubio et al. (2018) <doi:10.1007/978-3-030-01584-8_1>.

Several tests for differential methylation in methylation array data, including one-sided differential mean and variance test. Methods used in the package refer to Dai, J, Wang, X, Chen, H and others (2021) "Incorporating increased variability in discovering cancer methylation markers", Biostatistics, submitted.

Microsoft Word docx files provide an XML structure that is fairly straightforward to navigate, especially when it applies to Word tables and comments. Tools are provided to determine table count/structure, comment count and also to extract/clean tables and comments from Microsoft Word docx documents. There is also nascent support for .doc and .pptx files.

Allows manual creation of themes and logos to be used in applications created using the shinydashboard package. Removes the need to change the underlying css code by wrapping it into a set of convenient R functions.

Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the <https://www.shinyapps.io/>. Current version of DynNom supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, and mgcv::gam model objects.

This package provides an R interface to the Drogon high-performance C++ HTTP server framework (<https://github.com/drogonframework/drogon>). Offers a plumber'-style application programming interface for building REST services from R with substantially higher throughput.

Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation.

Efficient computation of multivariate statistical depth functions in arbitrary dimension d. Implements Mahalanobis depth, Tukey (halfspace) depth, Liu simplicial depth (via adaptive Monte Carlo), projection depth, and spatial depth. Provides depth-based medians, central regions, outlier detection, and depth-depth plots. C++ backends via Rcpp and RcppEigen ensure performance at large n and d. References: Liu (1990) <doi:10.1214/aos/1176347507>, Zuo and Serfling (2000) <doi:10.1214/aos/1016218226>, Vardi and Zhang (2000) <doi:10.1073/pnas.97.4.1423>.