This package provides programmatic access to GitHub API with a focus on project management. Key functionality includes setting up issues and milestones from R objects or YAML configurations, querying outstanding or completed tasks, and generating progress updates in tables, charts, and RMarkdown reports. Useful for those using GitHub in personal, professional, or academic settings with an emphasis on streamlining the workflow of data analysis projects.

Bayesian analysis of censored linear mixed-effects models that replace Gaussian assumptions with a flexible class of distributions, such as the scale mixture of normal family distributions, considering a damped exponential correlation structure which was employed to account for within-subject autocorrelation among irregularly observed measures. For more details, see Kelin Zhong, Fernanda L. Schumacher, Luis M. Castro, Victor H. Lachos (2025) <doi:10.1002/sim.10295>.

Quality control charts for survival outcomes. Allows users to construct the Continuous Time Generalized Rapid Response CUSUM (CGR-CUSUM) <doi:10.1093/biostatistics/kxac041>, the Biswas & Kalbfleisch (2008) <doi:10.1002/sim.3216> CUSUM, the Bernoulli CUSUM and the risk-adjusted funnel plot for survival data <doi:10.1002/sim.1970>. These procedures can be used to monitor survival processes for a change in the failure rate.

This package provides a geomorphology-based hydrological modelling for transferring streamflow measurements from gauged to ungauged catchments. Inverse modelling enables to estimate net rainfall from streamflow measurements following Boudhraâ et al. (2018) <doi:10.1080/02626667.2018.1425801>. Resulting net rainfall is then estimated on the ungauged catchments by spatial interpolation in order to finally simulate streamflow following de Lavenne et al. (2016) <doi:10.1002/2016WR018716>.

Estimators for semi-parametric linear regression models with truncated response variables (fixed truncation point). The estimators implemented are the Symmetrically Trimmed Least Squares (STLS) estimator introduced by Powell (1986) <doi:10.2307/1914308>, the Quadratic Mode (QME) estimator introduced by Lee (1993) <doi:10.1016/0304-4076(93)90056-B>, and the Left Truncated (LT) estimator introduced by Karlsson (2006) <doi:10.1007/s00184-005-0023-x>.

This package provides a library for creating time based charts, like Gantt or timelines. Possible outputs include ggplot2 diagrams, plotly.js graphs, Highcharts.js widgets and data.frames. Results can be used in the RStudio viewer pane, in RMarkdown documents or in Shiny apps. In the interactive outputs created by vistime() and hc_vistime(), you can interact with the plot using mouse hover or zoom.

This package provides a set of tools for creation, manipulation, and modeling of tensors with arbitrary number of modes. A tensor in the context of data analysis is a multidimensional array. rTensor does this by providing a S4 class Tensor that wraps around the base array class. rTensor provides common tensor operations as methods, including matrix unfolding, summing/averaging across modes, calculating the Frobenius norm, and taking the inner product between two tensors. Familiar array operations are overloaded, such as index subsetting via [ and element-wise operations. rTensor also implements various tensor decomposition, including CP, GLRAM, MPCA, PVD, Tucker, INDSCAL, RESCAL, DEDICOM, PARAFAC2, and 2DLDA. For tensors with 3 modes, rTensor also implements transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some auxiliary functions include the Khatri-Rao product, Kronecker product, and the Hadamard product for a list of matrices.

CaMutQC is able to filter false positive mutations generated due to technical issues, as well as to select candidate cancer mutations through a series of well-structured functions by labeling mutations with various flags. And a detailed and vivid filter report will be offered after completing a whole filtration or selection section. Also, CaMutQC integrates serveral methods and gene panels for Tumor Mutational Burden (TMB) estimation.

This package provides a correlation-based multiview self-organizing map for the characterization of cell types in highly multiplexed in situ imaging cytometry assays (`FuseSOM`) is a tool for unsupervised clustering. `FuseSOM` is robust and achieves high accuracy by combining a `Self Organizing Map` architecture and a `Multiview` integration of correlation based metrics. This allows FuseSOM to cluster highly multiplexed in situ imaging cytometry assays.

This package provides functions for bootstrapping with multilevel data and models (and mixed-effect models). It implements multiple bootstrap methods under the parametric, residual, and case bootstrap categories, as discussed in Van der Leeden, Meijer, and Busing (2008) <doi:10.1007/978-0-387-73186-5_11> and Carpenter, Goldstein, and Rasbash (2003) <doi:10.1111/1467-9876.00415>. Currently it supports fitted objects from the lme4 package.

Automatize downloading of meteorological and hydrological data from publicly available repositories: OGIMET (<http://ogimet.com/index.phtml.en>), University of Wyoming - atmospheric vertical profiling data (<http://weather.uwyo.edu/upperair/>), Polish Institute of Meteorology and Water Management - National Research Institute (<https://danepubliczne.imgw.pl>), and National Oceanic & Atmospheric Administration (NOAA). This package also allows for searching geographical coordinates for each observation and calculate distances to the nearest stations.

An extension of knitr that adds flexibility in several ways. One common source of frustration with knitr is that it assumes the directory where the source file lives should be the working directory, which is often not true. ezknitr addresses this problem by giving you complete control over where all the inputs and outputs are, and adds several other convenient features to make rendering markdown/HTML documents easier.

This package implements the AdaptiveImpute matrix completion algorithm of Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion <doi:10.1080/10618600.2018.1518238> as well as the specialized variant of Co-Factor Analysis of Citation Networks <doi:10.1080/10618600.2024.2394464>. AdaptiveImpute is useful for embedding sparsely observed matrices, often out performs competing matrix completion algorithms, and self-tunes its hyperparameter, making usage easy.

This package provides a not-so-comprehensive list of methods for estimating graphon, a symmetric measurable function, from a single or multiple of observed networks. For a detailed introduction on graphon and popular estimation techniques, see the paper by Orbanz, P. and Roy, D.M.(2014) <doi:10.1109/TPAMI.2014.2334607>. It also contains several auxiliary functions for generating sample networks using various network models and graphons.

This package provides a compilation of functions to create visually appealing and information-rich plots of meta-analytic data using ggplot2'. Currently allows to create forest plots, funnel plots, and many of their variants, such as rainforest plots, thick forest plots, additional evidence contour funnel plots, and sunset funnel plots. In addition, functionalities for visual inference with the funnel plot in the context of meta-analysis are provided.

Enhances mlexperiments <https://CRAN.R-project.org/package=mlexperiments> with additional machine learning ('ML') learners. The package provides R6-based learners for the following algorithms: glmnet <https://CRAN.R-project.org/package=glmnet>, ranger <https://CRAN.R-project.org/package=ranger>, xgboost <https://CRAN.R-project.org/package=xgboost>, and lightgbm <https://CRAN.R-project.org/package=lightgbm>. These can be used directly with the mlexperiments R package.

Create native charts for Microsoft PowerPoint', Microsoft Excel and Microsoft Word documents. The resulting charts can then be edited and annotated in the host application. It provides functions to create charts and to modify their content and formatting. The chart's underlying data is automatically saved within the Word', Excel or PowerPoint file. It extends the officer package, which does not provide native Microsoft chart production.

Analyzing longitudinal clinical data from Electronic Health Records (EHRs) using linear mixed models (LMM) and visualizing the results as networks. It includes functions for fitting LMM, normalizing adjacency matrices, and comparing networks. The package is designed for researchers in clinical and biomedical fields who need to model longitudinal data and explore relationships between variables For more details see Bates et al. (2015) <doi:10.18637/jss.v067.i01>.

Requires rooted phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways. "MonoPhy: a simple R package to find and visualize monophyly issues." Schwery, O. & O'Meara, B.C. (2016) <doi:10.7717/peerj-cs.56>.

An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users). Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.

OpenTelemetry is a collection of tools, APIs, and SDKs used to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) for analysis in order to understand your software's performance and behavior. This package contains the OpenTelemetry SDK, and exporters. Use this package to export traces, metrics, logs from instrumented R code. Use the otel package to instrument your R code for OpenTelemetry.

Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach. Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.

This package provides a Shiny input widget, pasteBoxInput, that allows users to paste images directly into a Shiny application. The pasted images are captured as Base64 encoded strings and can be used within the application for various purposes, such as display or further processing. This package is particularly useful for applications that require easy and quick image uploads without the need for traditional file selection dialog boxes.

This package implements sparse regression with paired covariates (<doi:10.1007/s11634-019-00375-6>). The paired lasso is designed for settings where each covariate in one set forms a pair with a covariate in the other set (one-to-one correspondence). For the optional correlation shrinkage, install ashr (<https://github.com/stephens999/ashr>) and CorShrink (<https://github.com/kkdey/CorShrink>) from GitHub (see README).