This package provides a dashboard supports the usage of cromwell'. Cromwell is a scientific workflow engine for command line users. This package utilizes cromwell REST APIs and provides these convenient functions: timing diagrams for running workflows, cromwell engine status, a tabular workflow list. For more information about cromwell', visit <http://cromwell.readthedocs.io>.

An implementation of efficiency first conformal prediction (EFCP) and validity first conformal prediction (VFCP) that demonstrates both validity (coverage guarantee) and efficiency (width guarantee). To learn how to use it, check the vignettes for a quick tutorial. The package is based on the work by Yang Y., Kuchibhotla A.,(2021) <arxiv:2104.13871>.

Support for implicit expansion of arrays in operations involving arrays of mismatching sizes. This pattern is known as "broadcasting" in Python and "implicit expansion" in Matlab and is explained for example in the article "Array programming with NumPy" by C. R. Harris et al. (2020) <doi:10.1038/s41586-020-2649-2>.

This package provides a tm Source to create corpora from articles exported from the Dow Jones Factiva content provider as XML or HTML files. It is able to read both text content and meta-data information (including source, date, title, author, subject, geographical coverage, company, industry, and various provider-specific fields).

Minitest-hooks adds around, before_all, after_all, around_all hooks for Minitest. This allows, for instance, running each suite of specs inside a database transaction, running each spec inside its own savepoint inside that transaction. This can significantly speed up testing for specs that share expensive database setup code.

Post Global Financial Crisis derivatives reforms have lifted the veil off over-the-counter (OTC) derivative markets. Swap Execution Facilities (SEFs) and Swap Data Repositories (SDRs) now publish data on swaps that are traded on or reported to those facilities (respectively). This package provides you the ability to get this data from supported sources.

RocBandwidthTest is designed to capture the performance characteristics of buffer copying and kernel read/write operations. The help screen of the benchmark shows various options one can use in initiating copy/read/writer operations. In addition one can also query the topology of the system in terms of memory pools and their agents.

Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette.

Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model.

Speeds up exploratory data analysis (EDA) by providing a succinct workflow and interactive visualization tools for understanding which features have relationships to target (response). Uses binary correlation analysis to determine relationship. Default correlation method is the Pearson method. Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) <doi:10.1145/2637484>.

This package contains the prepared data that is needed for the shiny application examples in the canvasXpress package. This package also includes datasets used for automated testthat tests. Scotto L, Narayan G, Nandula SV, Arias-Pulido H et al. (2008) <doi:10.1002/gcc.20577>. Davis S, Meltzer PS (2007) <doi:10.1093/bioinformatics/btm254>.

Estimate bivariate common mean vector under copula models with known correlation. In the current version, available copulas are the Clayton, Gumbel, Frank, Farlie-Gumbel-Morgenstern (FGM), and normal copulas. See Shih et al. (2019) <doi:10.1080/02331888.2019.1581782> and Shih et al. (2021) <under review> for details under the FGM and general copulas, respectively.

This package provides SPSS- and SAS-like output for least squares multiple regression, logistic regression, and count variable regressions. Detailed output is also provided for OLS moderated regression, interaction plots, and Johnson-Neyman regions of significance. The output includes standardized coefficients, partial and semi-partial correlations, collinearity diagnostics, plots of residuals, and detailed information about simple slopes for interactions. The output for some functions includes Bayes Factors and, if requested, regression coefficients from Bayesian Markov Chain Monte Carlo analyses. There are numerous options for model plots. The REGIONS_OF_SIGNIFICANCE function also provides Johnson-Neyman regions of significance and plots of interactions for both lm and lme models. There is also a function for partial and semipartial correlations and a function for conducting Cohen's set correlation analyses.

This package provides a Shiny app including the Monaco editor. The Monaco editor is the code editor which powers VS Code'. It is particularly well developed for JavaScript'. In addition to the Monaco editor features, the app provides prettifiers and minifiers for multiple languages, SCSS and TypeScript compilers, code checking for C and C++ (requires cppcheck').

This package provides functions for graph-based multiple-sample testing and visualization of microbiome data, in particular data stored in phyloseq objects. The tests are based on those described in Friedman and Rafsky (1979) <http://www.jstor.org/stable/2958919>, and the tests are described in more detail in Callahan et al. (2016) <doi:10.12688/f1000research.8986.1>.

This package implements the algorithm introduced in Tian, Y., and Safikhani, A. (2024) <doi:10.5705/ss.202024.0182>, "Sequential Change Point Detection in High-dimensional Vector Auto-regressive Models". This package provides tools for detecting change points in the transition matrices of VAR models, effectively identifying shifts in temporal and cross-correlations within high-dimensional time series data.

MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework).

This package provides tools for designing and analyzing Acceptance Sampling plans. Supports both Attributes Sampling (Binomial and Poisson distributions) and Variables Sampling (Normal and Beta distributions), enabling quality control for fractional and compositional data. Uses nonlinear programming for sampling plan optimization, minimizing sample size while controlling producer's and consumer's risks. Operating Characteristic curves are available for plan visualization.

This package implements the cross-validation methodology from Pein and Shah (2021) <arXiv:2112.03220>. Can be customised by providing different cross-validation criteria, estimators for the change-point locations and local parameters, and freely chosen folds. Pre-implemented estimators and criteria are available. It also includes our own implementation of the COPPS procedure <doi:10.1214/19-AOS1814>.

This package implements a novel approach for measuring feature importance in k-means clustering. Importance of a feature is measured by the misclassification rate relative to the baseline cluster assignment due to a random permutation of feature values. An explanation of permutation feature importance in general can be found here: <https://christophm.github.io/interpretable-ml-book/feature-importance.html>.

Implementation of the algorithm introduced in Shah, R. D. (2016) <https://www.jmlr.org/papers/volume17/13-515/13-515.pdf>. Data with thousands of predictors can be handled. The algorithm performs sequential Lasso fits on design matrices containing increasing sets of candidate interactions. Previous fits are used to greatly speed up subsequent fits, so the algorithm is very efficient.

This package provides a bootstrap test which decides whether two dose response curves can be assumed as equal concerning their maximum absolute deviation. A plenty of choices for the model types are available, which can be found in the DoseFinding package, which is used for the fitting of the models. See <doi:10.1080/01621459.2017.1281813> for details.

This package creates visualization plots for 2D projected data including ellipse plots, Voronoi diagram plots, and combined ellipse-Voronoi plots. Designed to visualize class separation in dimensionally reduced data from techniques like principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) or others. For more details see Lotsch and Ultsch (2024) <doi:10.1016/j.imu.2024.101573>.

High dimensional discriminant analysis with compositional data is performed. The compositional data are first transformed using the alpha-transformation of Tsagris M., Preston S. and Wood A.T.A. (2011) <doi:10.48550/arXiv.1106.1451>, and then the High Dimensional Discriminant Analysis (HDDA) algorithm of Bouveyron C. Girard S. and Schmid C. (2007) <doi:10.1080/03610920701271095> is applied.