Enhances the R Optimization Infrastructure ('ROI') package with the quadratic solver qpOASES'. More information about qpOASES can be found at <https://github.com/coin-or/qpOASES>.

This package provides a checkbox group input for usage in a Shiny application. The checkbox group has a head checkbox allowing to check or uncheck all the checkboxes in the group. The checkboxes are customizable.

Summarise results from simulation studies and compute Monte Carlo standard errors of commonly used summary statistics. This package is modelled on the simsum user-written command in Stata (White I.R., 2010 <https://www.stata-journal.com/article.html?article=st0200>), further extending it with additional performance measures and functionality.

Captures errors encountered when running run_examples()', and processes and archives them. The function run_examples() within the devtools package allows batch execution of all of the examples within a given package. This is much more convenient than testing each example manually. However, a major inconvenience is that if an error is encountered, the program stops and does not complete testing the remaining examples. Also, there is not a systematic record of the results, namely which package functions had no examples, which had examples that failed, and which had examples that succeeded. The current package provides the missing functionality.

Easy-to-use functions for downloading air quality data from the Mexican National Air Quality Information System (SINAICA). Allows you to query pollution and meteorological parameters from more than a hundred monitoring stations located throughout Mexico. See <https://sinaica.inecc.gob.mx> for more information.

Resolve the dependency graph of R packages at a specific time point based on the information from various R-hub web services <https://blog.r-hub.io/>. The dependency graph can then be used to reconstruct the R computational environment with Rocker <https://rocker-project.org>.

Conversion between attitude representations: DCM, Euler angles, Quaternions, and Euler vectors. Plus conversion between 2 Euler angle set types (xyx, yzy, zxz, xzx, yxy, zyz, xyz, yzx, zxy, xzy, yxz, zyx). Fully vectorized code, with warnings/errors for Euler angles (singularity, out of range, invalid angle order), DCM (orthogonality, not proper, exceeded tolerance to unity determinant) and Euler vectors(not unity). Also quaternion and other useful functions. Based on SpinCalc by John Fuller and SpinConv by Paolo de Leva.

Studies of resilience in older adults employ a single-arm design where everyone experiences the stressor. The simplistic approach of regressing change versus baseline yields biased estimates due to regression-to-the-mean. This package provides a method to correct the bias. It also allows covariates to be included. The method implemented in the package is described in Varadhan, R., Zhu, J., and Bandeen-Roche, K (2024), Biostatistics 25(4): 1094-1111.

Set of utilities to facilitate the reproduction of analysis in R. It allow to make_structure(), clean_structure(), and run and log programs in a predefined order to allow secondary files, analysis and reports be constructed in an ordered and reproducible form.

This package provides a toolkit for analyzing classifier performance by using receiver operating characteristic (ROC) curves. Performance may be assessed on a single classifier or multiple ones simultaneously, making it suitable for comparisons. In addition, different metrics allow the evaluation of local performance when working within restricted ranges of sensitivity and specificity. For details on the different implementations, see McClish D. K. (1989) <doi:10.1177/0272989X8900900307>, Vivo J.-M., Franco M. and Vicari D. (2018) <doi:10.1007/S11634-017-0295-9>, Jiang Y., et al (1996) <doi:10.1148/radiology.201.3.8939225>, Franco M. and Vivo J.-M. (2021) <doi:10.3390/math9212826> and Carrington, André M., et al (2020) <doi: 10.1186/s12911-019-1014-6>.

The main purpose of this package is to perform simulation-based estimation of stochastic actor-oriented models for longitudinal network data collected as panel data. Dependent variables can be single or multivariate networks, which can be directed, non-directed, or two-mode; and associated actor variables. There are also functions for testing parameters and checking goodness of fit. An overview of these models is given in Snijders (2017), <doi:10.1146/annurev-statistics-060116-054035>.

Recursive lists in the form of R objects, JSON', and XML', for use in teaching and examples. Examples include color palettes, Game of Thrones characters, GitHub users and repositories, music collections, and entities from the Star Wars universe. Data from the gapminder package is also included, as a simple data frame and in nested and split forms.

This is a sudoku game package with a shiny application for playing .

Constructs various robust quality control charts based on the median or Hodges-Lehmann estimator (location) and the median absolute deviation (MAD) or Shamos estimator (scale). The estimators used for the robust control charts are all unbiased with a sample of finite size. For more details, see Park, Kim and Wang (2022) <doi:10.1080/03610918.2019.1699114>. In addition, using this R package, the conventional quality control charts such as X-bar, S, R, p, np, u, c, g, h, and t charts are also easily constructed. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C1091319).

Allows the user to generate and execute select, insert, update and delete SQL queries the underlying database without having to explicitly write SQL code.

An R interface to the typeform <https://www.typeform.com/> application program interface. Also provides functions for downloading your results.

An implementation of calls designed to collect Tumblr data via its Application Program Interfaces (API), which can be found at the following URL: <https://www.tumblr.com/docs/en/api/v2>.

This package provides Java graphical user interfaces for viewing, manipulating and plotting graphs. Graphs may be directed or undirected.

An optimized method for identifying mutually exclusive genomic events. Its main contribution is a statistical analysis based on the Poisson-Binomial distribution that takes into account that some samples are more mutated than others. See [Canisius, Sander, John WM Martens, and Lodewyk FA Wessels. (2016) "A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence." Genome biology 17.1 : 1-17. <doi:10.1186/s13059-016-1114-x>]. The mutations matrices are sparse matrices. The method developed takes advantage of the advantages of this type of matrix to save time and computing resources.

An implementation of Bayesian model-averaged t-tests that allows users to draw inferences about the presence versus absence of an effect, variance heterogeneity, and potential outliers. The RoBTT package estimates ensembles of models created by combining competing hypotheses and applies Bayesian model averaging using posterior model probabilities. Users can obtain model-averaged posterior distributions and inclusion Bayes factors, accounting for uncertainty in the data-generating process (Maier et al., 2024, <doi:10.3758/s13423-024-02590-5>). The package also provides a truncated likelihood version of the model-averaged t-test, enabling users to exclude potential outliers without introducing bias (Godmann et al., 2024, <doi:10.31234/osf.io/j9f3s>). Users can specify a wide range of informative priors for all parameters of interest. The package offers convenient functions for summary, visualization, and fit diagnostics.

Defines the underlying pipeline structure for reproducible neuroscience, adopted by RAVE (reproducible analysis and visualization of intracranial electroencephalography); provides high-level class definition to build, compile, set, execute, and share analysis pipelines. Both R and Python are supported, with Markdown and shiny dashboard templates for extending and building customized pipelines. See the full documentations at <https://rave.wiki>; to cite us, check out our paper by Magnotti, Wang, and Beauchamp (2020, <doi:10.1016/j.neuroimage.2020.117341>), or run citation("ravepipeline") for details.

Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variability as possible. By transforming the original variables into a new set of uncorrelated variables called principal components, PCA helps in identifying patterns and simplifying the complexity of high-dimensional data. The RankPCA package provides a streamlined workflow for performing PCA on datasets containing both categorical and continuous variables. It facilitates data preprocessing, encoding of categorical variables, and computes PCA to determine the optimal number of principal components based on a specified variance threshold. The package also computes composite indices for ranking observations, which can be useful for various analytical purposes. Garai, S., & Paul, R. K. (2023) <doi:10.1016/j.iswa.2023.200202>.

This package provides functions to obtain an important number of electoral indicators described in the package, which can be divided into two large sections: The first would be the one containing the indicators of electoral disproportionality, such as, Rae index, Loosemoreâ Hanby index, etc. The second group is intended to study the dimensions of the party system vote, through the indicators of electoral fragmentation, polarization, volatility, etc. Moreover, multiple seat allocation simulations can also be performed based on different allocation systems, such as the D'Hondt method, Sainte-Laguë, etc. Finally, some of these functions have been built so that, if the user wishes, the data provided by the Spanish Ministry of Home Office for different electoral processes held in Spain can be obtained automatically. All the above will allow the users to carry out deep studies on the results obtained in any type of electoral process. The methods are described in: Oñate, Pablo and Ocaña, Francisco A. (1999, ISBN:9788474762815); Ruiz Rodrà guez, Leticia M. and Otero Felipe, Patricia (2011, ISBN:9788474766226).

Bindings to kernel methods for enforcing security restrictions. AppArmor can apply mandatory access control (MAC) policies on a given task (process) via security profiles with detailed ACL definitions. In addition this package implements bindings for setting process resource limits (rlimit), uid, gid, affinity and priority. The high level R function eval.secure builds on these methods to perform dynamic sandboxing: it evaluates a single R expression within a temporary fork which acts as a sandbox by enforcing fine grained restrictions without affecting the main R process. A portable version of this function is now available in the unix package.