Most function focus on specific ways to customize a graph. They use a qgraph output as the first argument, and return a modified qgraph object. This allows the functions to be chained by a pipe operator.

The President of the United States is constitutionally obligated to provide a report known as the State of the Union'. The report summarizes the current challenges facing the country and the president's upcoming legislative agenda. While historically the State of the Union was often a written document, in recent decades it has always taken the form of an oral address to a joint session of the United States Congress. This package provides the raw text from every such address with the intention of being used for meaningful examples of text analysis in R. The corpus is well suited to the task as it is historically important, includes material intended to be read and material intended to be spoken, and it falls in the public domain. As the corpus spans over two centuries it is also a good test of how well various methods hold up to the idiosyncrasies of historical texts. Associated data about each address, such as the year, president, party, and format, are also included.

Estimation and inference for parameters in a Gaussian copula model, treating the univariate marginal distributions as nuisance parameters as described in Hoff (2007) <doi:10.1214/07-AOAS107>. This package also provides a semiparametric imputation procedure for missing multivariate data.

Clinical Data Interchange Standards Consortium (CDISC) Standard Data Tabulation Model (SDTM) controlled terminology, 2025-03-25. Source: <https://evs.nci.nih.gov/ftp1/CDISC/SDTM/>.

The implementation of the algorithm for estimation of mutual information and channel capacity from experimental data by classification procedures (logistic regression). Technically, it allows to estimate information-theoretic measures between finite-state input and multivariate, continuous output. Method described in Jetka et al. (2019) <doi:10.1371/journal.pcbi.1007132>.

This package provides a covariance estimator for multivariate normal data that is sparse and positive definite. Implements the majorize-minimize algorithm described in Bien, J., and Tibshirani, R. (2011), "Sparse Estimation of a Covariance Matrix," Biometrika. 98(4). 807--820.

This package provides functions that wrap HTML Bootstrap components code to enable the design and layout of informative landing home pages for Shiny applications. This can lead to a better user experience for the users and writing less HTML for the developer.

Univariate time series forecasting with STL decomposition based auto regressive integrated moving average (ARIMA) hybrid model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

This package provides a user-friendly framework for estimating a wide variety of cross-sectional and panel stochastic frontier models. Suitable for a broad range of applications, the implementation offers extensive flexibility in specification and estimation techniques.

Connecting to databases requires boilerplate code to specify connection parameters and to set up sessions properly with the DBMS. This package provides a simple tool to fill two purposes: abstracting connection details, including secret credentials, out of your source code and managing configuration for frequently-used database connections in a persistent and flexible way, while minimizing requirements on the runtime environment.

This package provides a flexible framework combining variable screening and random projection techniques for fitting ensembles of predictive generalized linear models to high-dimensional data. Designed for extensibility, the package implements key techniques as S3 classes with user-friendly constructors, enabling easy integration and development of new procedures for high-dimensional applications. For more details see Parzer et al (2024a) <doi:10.48550/arXiv.2312.00130> and Parzer et al (2024b) <doi:10.48550/arXiv.2410.00971>.

Automatic generation and selection of spatial predictors for Random Forest models fitted to spatially structured data. Spatial predictors are constructed from a distance matrix among training samples using Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>) or the RFsp approach (Hengl et al. <DOI:10.7717/peerj.5518>). These predictors are used alongside user-supplied explanatory variables in Random Forest models. The package provides functions for model fitting, multicollinearity reduction, interaction identification, hyperparameter tuning, evaluation via spatial cross-validation, and result visualization using partial dependence and interaction plots. Model fitting relies on the ranger package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>).

Execute the self-controlled case series (SCCS) design using observational data in the OMOP Common Data Model. Extracts all necessary data from the database and transforms it to the format required for SCCS. Age and season can be modeled using splines assuming constant hazard within calendar months. Event-dependent censoring of the observation period can be corrected for. Many exposures can be included at once (MSCCS), with regularization on all coefficients except for the exposure of interest. Includes diagnostics for all major assumptions of the SCCS.

This package provides functions related to multivariate measures of independence and ICA: -estimate independent components by minimizing distance covariance; -conduct a test of mutual independence based on distance covariance; -estimate independent components via infomax (a popular method but generally performs poorer than mdcovica, ProDenICA, and/or fastICA, but is useful for comparisons); -order indepedent components by skewness; -match independent components from multiple estimates; -other functions useful in ICA.

An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See <http://snap.stanford.edu/seismic/> for more information and datasets.

Enhance the bookmarkable state feature of shiny with additional customization such as storage location and storage repositories leveraging the pins package.

This package provides a general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort. The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results. It follows a clear object-oriented design and supports parallel computing to increase computational performance.

This package implements the algorithm described in Barron, M., and Li, J. (Not yet published). This algorithm clusters samples from multiple ordered populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseMDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers.

The objective of these functions is to derive a species assemblage that satisfies a functional trait profile. Restoring resilient ecosystems requires a flexible framework for selecting assemblages that are based on the functional traits of species. However, current trait-based models have been limited to algorithms that can only select species by optimising specific trait values, and could not elegantly accommodate the common desire among restoration ecologists to produce functionally diverse assemblages. We have solved this problem by applying a non-linear optimisation algorithm that optimises Rao Q, a closed-form functional trait diversity index that incorporates species abundances, subject to other linear constraints. This framework generalises previous models that only optimised the entropy of the community, and can optimise both functional diversity and entropy simultaneously. This package can also be used to generate experimental assemblages to test the effects of community-level traits on community dynamics and ecosystem function. The method is based on theory discussed in Laughlin (2014, Ecology Letters) and Laughlin et al. (2018, Methods in Ecology and Evolution).

Algorithm to estimate the Sobol indices using a non-parametric fit of the regression curve. The bandwidth is estimated using bootstrap to reduce the finite-sample bias. The package is based on the paper Solà s, M. (2018) <arXiv:1803.03333>.

This package provides methods for spatial risk calculations, focusing on efficient determination of the sum of observations within a circle of a given radius. These methods are particularly relevant for applications such as insurance, where recent European Commission regulations require the calculation of the maximum insured value of fire risk policies for all buildings that are partly or fully located within a 200 m radius. The underlying problem is described by Church (1974) <doi:10.1007/BF01942293>.

Generalized additive models under shape constraints on the component functions of the linear predictor. Models can include multiple shape-constrained (univariate and bivariate) and unconstrained terms. Routines of the package mgcv are used to set up the model matrix, print, and plot the results. Multiple smoothing parameter estimation by the Generalized Cross Validation or similar. See Pya and Wood (2015) <doi:10.1007/s11222-013-9448-7> for an overview. A broad selection of shape-constrained smoothers, linear functionals of smooths with shape constraints, and Gaussian models with AR1 residuals.

Facilitates probabilistic record linkage between infectious disease surveillance datasets (notifiable disease registers, outbreak line-lists), vaccination registries, and hospitalization records using methods based on Fellegi and Sunter (1969) <doi:10.1080/01621459.1969.10501049> and Sayers et al. (2016) <doi:10.1093/ije/dyv322>. The package provides core functions for data preparation, linkage, and analysis: clean_the_nest() standardizes variable names and formats across heterogeneous datasets; murmuration() performs machine learning-based record linkage using blocking variables and similarity metrics; molting() deidentifies datasets for secure sharing; homing() re-identifies previously deidentified datasets; plumage() identifies and categorizes comorbidities; and preening() creates analysis-ready variables including age categories and temporal groupings. Designed for epidemiological research linking acute and post-acute disease outcomes to vaccination status and healthcare utilization. Supports multiple linkage scenarios including case-to-vaccination, case-to-hospitalization, and event-based vaccination status determination (e.g., outbreak attendees, flight passengers, exposure site visitors).

This package provides functions are provided for internal use by the spatial capture-recapture package secr (from version 5.4.0). The idea is to speed up the installation of secr', and possibly reduce its size. Initially the functions are those for area and transect search that use numerical integration code from RcppNumerical and RcppEigen'. The functions are not intended to be user-friendly and require considerable preprocessing of data.