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>.

Dual Wavelet based Nonlinear Autoregressive Distributed Lag model has been developed for noisy time series analysis. This package is designed to capture both short-run and long-run relationships in time series data, while incorporating wavelet transformations. The methodology combines the NARDL model with wavelet decomposition to better capture the nonlinear dynamics of the series and exogenous variables. The package is useful for analyzing economic and financial time series data that exhibit both long-term trends and short-term fluctuations. This package has been developed using algorithm of Jammazi et al. <doi:10.1016/j.intfin.2014.11.011>.

Use numerical optimization to fit ordinary differential equations (ODEs) to time series data to examine the dynamic relationships between variables or the characteristics of a dynamical system. It can now be used to estimate the parameters of ODEs up to second order, and can also apply to multilevel systems. See <https://github.com/yueqinhu/defit> for details.

When visualising changes between two values over time, a strict linear interpolation can look jarring and unnatural. By applying a non-linear easing to the transition, the motion between values can appear smoother and more natural. This package includes functions for applying such non-linear easings to colors and numeric values, and is useful where smooth animated movement and transitions are desired.

Makes deck.gl <https://deck.gl/>, a WebGL-powered open-source JavaScript framework for visual exploratory data analysis of large datasets, available within R via the htmlwidgets package. Furthermore, it supports basemaps from mapbox <https://www.mapbox.com/> via mapbox-gl-js <https://github.com/mapbox/mapbox-gl-js>.

Offers robust tools to identify and manage incomplete responses in survey datasets, thereby enhancing the quality and reliability of research findings.

The desirable Dietary Pattern (DDP)/ PPH score measures the variety of food consumption. The (weighted) score is calculated based on the type of food. This package is intended to calculate the DDP/ PPH score that is faster than traditional method via a manual calculation by BKP (2017) <http://bkp.pertanian.go.id/storage/app/uploads/public/5bf/ca9/06b/5bfca906bc654274163456.pdf> and is simpler than the nutrition survey <http://www.nutrisurvey.de>. The database to create weights and baseline values is the Indonesia national survey in 2017.

This package provides methods for reading, displaying, processing and writing files originally arranged for the DSSAT-CSM fixed width format. The DSSAT-CSM cropping system model is described at J.W. Jones, G. Hoogenboomb, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, J.T. Ritchie (2003) <doi:10.1016/S1161-0301(02)00107-7>.

Joint DNA-based disaster victim identification (DVI), as described in Vigeland and Egeland (2021) <doi:10.21203/rs.3.rs-296414/v1>. Identification is performed by optimising the joint likelihood of all victim samples and reference individuals. Individual identification probabilities, conditional on all available information, are derived from the joint solution in the form of posterior pairing probabilities. dvir is part of the pedsuite collection of packages for pedigree analysis.

This package provides tools to estimate and manage empirical distributions, which should work with survey data. One of the main features is the possibility to create data cubes of estimated statistics, that include all the combinations of the variables of interest (see for example functions dcc5() and dcc6()).

This package implements discrete curvature estimation for ordered planar point sequences using circumcenter geometry on consecutive triplets, exposed through compiled C plus plus (C++) code via Rcpp for speed and numerical robustness. The package is useful for objective elbow detection in multivariate workflows, especially principal component analysis (PCA), where selecting the number of retained components can be subjective. It provides a shiny interface that supports upload of raw datasets or explained-variance tables, computes Kaiser-Meyer-Olkin (KMO) sampling-adequacy diagnostics, evaluates individual and cumulative variance curves, and reports curvature- based decision rules (m* and m**) with visual summaries for reproducible component-selection decisions. References: Arney et al. (2001); Axler (2024) <doi:10.1007/978-3-031-41026-0>; Bjorklund (2019) <doi:10.1111/evo.13835>; Burden and Faires (2015); Chang et al. (2023) <https://CRAN.R-project.org/package=shiny>; Christensen (2019); Cui (2020) <doi:10.18637/jss.v040.i08>; Eddelbuettel and Sanderson (2014) <doi:10.1016/j.csda.2013.02.005>; Engelke et al. (2023) <doi:10.1016/j.jseint.2023.04.010>; Gniazdowski (2021) <doi:10.26348/znwwsi.24.35>; Haynes et al. (2017); Jameel and Al-Salami (2023) <doi:10.24086/cuejhss.v7n1y2023.pp121-125>; Jolliffe (2002); Jolliffe and Cadima (2016) <doi:10.1098/rsta.2015.0202>; Kaiser (1974); Lehnert et al. (2019) <doi:10.18637/jss.v089.i12>; Ma and Dai (2011) <doi:10.1093/bib/bbq090>; Milligan (1995); Onumanyi et al. (2022) <doi:10.3390/app12157515>; Park (2010); Revelle (2024) <https://CRAN.R-project.org/package=psych>; Rodionova et al. (2021) <doi:10.1016/j.chemolab.2021.104304>; Sen and Cohen (2025) <doi:10.1177/01466216251344288>; Serneels and Verdonck (2008) <doi:10.1016/j.csda.2007.05.024>; Shi et al. (2021) <doi:10.1186/s13638-021-01910-w>; Shaukat et al. (2016) <doi:10.1515/eko-2016-0014>; Syakur et al. (2018) <doi:10.1088/1757-899X/336/1/012017>; Wickham and Bryan (2023) <https://CRAN.R-project.org/package=readxl>; Wu et al. (2017) <doi:10.1088/1755-1315/61/1/012054>; Youssef et al. (2023) <doi:10.21303/2461-4262.2023.002582>.

This package provides R-implementation of Decision forest algorithm, which combines the predictions of multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel pattern-recognition method which can be used to analyze: (1) DNA microarray data; (2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) data; and (3) Structure-Activity Relation (SAR) data. In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV. run Dforest() to see more instructions. Weida Tong (2003) <doi:10.1021/ci020058s>.

Fit a Poisson regression to carcass distance data and integrate over the searched area at a wind farm to estimate the fraction of carcasses falling in the searched area and format the output for use as the dwp parameter in the GenEst or eoa package for estimating bird and bat mortality, following Dalthorp, et al. (2024) <doi:10.3133/tm7A3>.

Access Datastream content through <https://product.datastream.com/dswsclient/Docs/Default.aspx>., our historical financial database with over 35 million individual instruments or indicators across all major asset classes, including over 19 million active economic indicators. It features 120 years of data, across 175 countries â the information you need to interpret market trends, economic cycles, and the impact of world events. Data spans bond indices, bonds, commodities, convertibles, credit default swaps, derivatives, economics, energy, equities, equity indices, ESG, estimates, exchange rates, fixed income, funds, fundamentals, interest rates, and investment trusts. Unique content includes I/B/E/S Estimates, Worldscope Fundamentals, point-in-time data, and Reuters Polls. Alongside the content, sit a set of powerful analytical tools for exploring relationships between different asset types, with a library of customizable analytical functions. In-house timeseries can also be uploaded using the package to comingle with Datastream maintained datasets, use with these analytical tools and displayed in Datastreamâ s flexible charting facilities in Microsoft Office.

This package provides a comprehensive framework for analyzing diversity from frequency/abundance count data. Implements a wide range of classical and entropy-based diversity indices, including Berger-Parker, Simpson (and related variants), Shannon, Brillouin, McIntosh, Margalef, Menhinick and Smith-Wilson. Supports permutation-based hypothesis tests for comparing groups with respect to diversity (global and pairwise comparisons), as well as confidence interval estimation using multiple bootstrap methods. Includes functionality for generating diversity profiles based on parametric families such as Hill numbers, Rényi entropy, and Tsallis entropy. The methods are applicable to ecological community data (species abundance counts) and genetic or phenotypic class frequency data.

The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group. Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time. This package contains tools for computing average treatment effect parameters in Difference in Differences setups with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>. The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a particular time. These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these. There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.

DataSHIELD is an infrastructure and series of R packages that enables the remote and non-disclosive analysis of sensitive research data. This package is the DataSHIELD interface implementation to analyze data shared on a MOLGENIS Armadillo server. MOLGENIS Armadillo is a light-weight DataSHIELD server using a file store and an RServe server.

This package provides a suite of tools are provided here to support authors in making their research more discoverable. check_keywords() - this function checks the keywords to assess whether they are already represented in the title and abstract. check_fields() - this function compares terminology used across the title, abstract and keywords to assess where terminological diversity (i.e. the use of synonyms) could increase the likelihood of the record being identified in a search. The function looks for terms in the title and abstract that also exist in other fields and highlights these as needing attention. suggest_keywords() - this function takes a full text document and produces a list of unigrams, bigrams and trigrams (1-, 2- or 2-word phrases) present in the full text after removing stop words (words with a low utility in natural language processing) that do not occur in the title or abstract that may be suitable candidates for keywords. suggest_title() - this function takes a full text document and produces a list of the most frequently used unigrams, bigrams and trigrams after removing stop words that do not occur in the abstract or keywords that may be suitable candidates for title words. check_title() - this function carries out a number of sub tasks: 1) it compares the length (number of words) of the title with the mean length of titles in major bibliographic databases to assess whether the title is likely to be too short; 2) it assesses the proportion of stop words in the title to highlight titles with low utility in search engines that strip out stop words; 3) it compares the title with a given sample of record titles from an .ris import and calculates a similarity score based on phrase overlap. This highlights the level of uniqueness of the title. This version of the package also contains functions currently in a non-CRAN package called litsearchr <https://github.com/elizagrames/litsearchr>.

This package provides a method to detect values poorly explained by a Gaussian linear model. The procedure is based on the maximum of the absolute value of the studentized residuals, which is a parameter-free statistic. This approach generalizes several procedures used to detect abnormal values during longitudinal monitoring of biological markers. For methodological details, see: Berthelot G., Saulière G., Dedecker J. (2025). "DEViaN-LM An R Package for Detecting Abnormal Values in the Gaussian Linear Model". HAL Id: hal-05230549. <https://hal.science/hal-05230549>.

This package contains data sets, examples and software from the Second Edition of "Design of Observational Studies"; see Rosenbaum, P.R. (2010) <doi:10.1007/978-1-4419-1213-8>.