High level management of widgets, windows and other graphical resources.

Minimal R client for the Screenshotbase API to render website screenshots and query account status. Provides functions to set the API key, call the status endpoint, and take a screenshot as a raw image response.

This package provides functionality to generate, (interactively) modify (by adding, removing and renaming nodes) and convert nested hierarchies between different formats. These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control.

This package provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.

Interactively play a game of sokoban ,which has nine game levels.Sokoban is a type of transport puzzle, in which the player pushes boxes or crates around in a warehouse, trying to get them to storage locations.

This package provides option settings management that goes beyond R's default options function. With this package, users can define their own option settings manager holding option names, default values and (if so desired) ranges or sets of allowed option values that will be automatically checked. Settings can then be retrieved, altered and reset to defaults with ease. For R programmers and package developers it offers cloning and merging functionality which allows for conveniently defining global and local options, possibly in a multilevel options hierarchy. See the package vignette for some examples concerning functions, S4 classes, and reference classes. There are convenience functions to reset par() and options() to their factory defaults'.

Taxonomic dictionaries, formative element lists, and functions related to the maintenance, development and application of U.S. Soil Taxonomy. Data and functionality are based on official U.S. Department of Agriculture sources including the latest edition of the Keys to Soil Taxonomy. Descriptions and metadata are obtained from the National Soil Information System or Soil Survey Geographic databases. Other sources are referenced in the data documentation. Provides tools for understanding and interacting with concepts in the U.S. Soil Taxonomic System. Most of the current utilities are for working with taxonomic concepts at the "higher" taxonomic levels: Order, Suborder, Great Group, and Subgroup.

This package provides a search interface to look up terms on Google', Bing', DuckDuckGo', Startpage', Ecosia', rseek', Twitter', StackOverflow', RStudio Community', GitHub', and BitBucket'. Upon searching, a browser window will open with the aforementioned search results.

Convert laboratory data to the Portuguese Information System for Water Resources SNIRH file format. SNIRH is Portugal's national water resources information system <https://snirh.apambiente.pt/>. The package validates station data, converts parameters and units, and generates compliant output files for data submission.

This package provides tools for simulating spatially dependent predictors (continuous or binary), which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous predictors are generated using traditional multivariate normal distributions or Gauss Markov random fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288> and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors exhibiting spatial clustering can also be easily specified by the user.

This package provides data frames that hold certain columns and attributes persistently for data processing in dplyr'.

This package provides R bindings for the Stencila Schema <https://schema.stenci.la>. This package is primarily aimed at R developers wanting to programmatically generate, or modify, executable documents.

To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects (the default specification assumes no-time-effect) under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) <doi:10.1093/biostatistics/kxy031>, Li et al. (2018) <doi:10.1111/biom.12918>. Supplementary documents can be found at: <https://ysph.yale.edu/cmips/research/software/study-design-power-calculation/swdpwr/>. The Shiny app for swdpwr can be accessed at: <https://jiachenchen322.shinyapps.io/swdpwr_shinyapp/>. The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively.

Handle POST requests on a custom path (e.g., /ingress) inside the same shiny HTTP server using user interface functions and HTTP responses. Expose latest payload as a reactive and provide helpers for query parameters.

The goal of SAFEPG is to predict climate-related extreme losses by fitting a frequency-severity model. It improves predictive performance by introducing a sign-aligned regularization term, which ensures consistent signs for the coefficients across the frequency and severity components. This enhancement not only increases model accuracy but also enhances its interpretability, making it more suitable for practical applications in risk assessment.

Includes general data manipulation functions, algorithms for statistical disclosure control (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6> and functions for hierarchical computations by sparse model matrices (Langsrud, 2023) <doi:10.32614/RJ-2023-088>.

Implementation of analytical models for estimating streamflow depletion due to groundwater pumping, and other related tools. Functions are broadly split into two groups: (1) analytical streamflow depletion models, which estimate streamflow depletion for a single stream reach resulting from groundwater pumping; and (2) depletion apportionment equations, which distribute estimated streamflow depletion among multiple stream reaches within a stream network. See Zipper et al. (2018) <doi:10.1029/2018WR022707> for more information on depletion apportionment equations and Zipper et al. (2019) <doi:10.1029/2018WR024403> for more information on analytical depletion functions, which combine analytical models and depletion apportionment equations.

An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) <doi:10.1007/978-3-642-04274-4_13>. Users can define which set of regressors to use as base models from the caret package, other packages, or custom functions.

Facilitates extraction of geospatial data from the Office for National Statistics Open Geography and nomis Application Programming Interfaces (APIs). Simplifies process of querying nomis datasets <https://www.nomisweb.co.uk/> and extracting desired datasets in dataframe format. Extracts area shapefiles at chosen resolution from Office for National Statistics Open Geography <https://geoportal.statistics.gov.uk/>.

Extends the SelectBoost approach to Generalized Additive Models for Location, Scale and Shape (GAMLSS). Implements bootstrap stability-selection across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC(). Includes optional standardization of predictors and helper functions for corrected AIC calculation. More details can be found in Bertrand and Maumy (2024) <https://hal.science/hal-05352041> that highlights correlation-aware resampling to improve variable selection for GAMLSS and quantile regression when predictors are numerous and highly correlated.

This package provides an application that acts as a GUI for the stm text analysis package.

This package performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. Reliability indicators such as reliability, maintainability, availability, BMP-failure rate, RG-failure rate, mean time to failure and mean time to repair are available as well. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. Estimation and simulation of discrete-time k-th order Markov chains are also considered.

Scale alignment is a new procedure for rescaling dimensions of between-items multidimensional Rasch family models so that dimensions scores can be compared directly (Feuerstahler & Wilson, 2019; under review) <doi:10.1111/jedm.12209>. This package includes functions for implementing delta-dimensional alignment (DDA) and logistic regression alignment (LRA) for dichotomous or polytomous data. This function also includes a wrapper for models fit using the TAM package.

Surveys to collect employment data so as to obtain data estimates on the number of employed people, the number of unemployed, and other employment indicators.