Conducts mediation analysis for structural equation models (SEM) estimated with lavaan', blavaan', cSEM', or modsem'. Implements the Baron and Kenny (1986) <doi:10.1037/0022-3514.51.6.1173> and Zhao, Lynch & Chen (2010) <doi:10.1086/651257> approaches to determine the presence and type of mediation. Supports covariance-based SEM, partial least squares SEM, Bayesian SEM, and moderated mediation models. Reports indirect effects with standard errors from Sobel, Delta, Monte-Carlo, and bootstrap methods, along with effect size measures (RIT, RID).

Download, inspect, reconcile, and summarize mammal taxonomic names with the Mammal Diversity Database (MDD). Supports accepted names, synonyms, original combinations, distribution summaries, and mapped outputs derived from packaged MDD releases. Designed for reproducible mammal name resolution workflows in R'.

NCL (NCAR Command Language) is one of the most popular spatial data mapping tools in meteorology studies, due to its beautiful output figures with plenty of color palettes designed by experts <https://www.ncl.ucar.edu/index.shtml>. Here we translate all NCL color palettes into R hexadecimal RGB colors and provide color selection function, which will help users make a beautiful figure.

Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order tensor time series, and have wide applications in economics, finance and medical imaging. We propose an one-step projection estimator by minimizing the least-square loss function, and further propose a robust estimator with an iterative weighted projection technique by utilizing the Huber loss function. The methods are discussed in Barigozzi et al. (2022) <arXiv:2206.09800>, and Barigozzi et al. (2023) <arXiv:2303.18163>.

This package provides a set of tools to streamline data analysis. Learning both R and introductory statistics at the same time can be challenging, and so we created rigr to facilitate common data analysis tasks and enable learners to focus on statistical concepts. We provide easy-to-use interfaces for descriptive statistics, one- and two-sample inference, and regression analyses. rigr output includes key information while omitting unnecessary details that can be confusing to beginners. Heteroscedasticity-robust ("sandwich") standard errors are returned by default, and multiple partial F-tests and tests for contrasts are easy to specify. A single regression function can fit both linear and generalized linear models, allowing students to more easily make connections between different classes of models.

This package performs one-sample t-test based on robustified statistics using median/MAD (TA) and Hodges-Lehmann/Shamos (TB). For more details, see Park and Wang (2018)<arXiv:1807.02215>. This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169).

The R Analytic Tool To Learn Easily (Rattle) provides a collection of utilities functions for the data scientist. This package (v5.6.0) supports the companion graphical interface with the aim to provide a simple and intuitive introduction to R for data science, allowing a user to quickly load data from a CSV file transform and explore the data, and to build and evaluate models. A key aspect of the GUI is that all R commands are logged and commented through the log tab. This can be saved as a standalone R script file and as an aid for the user to learn R or to copy-and-paste directly into R itself. If you want to use the older Rattle implementing the GUI in RGtk2 (which is no longer available from CRAN) then please install the Rattle package v5.5.1. See rattle.togaware.com for instructions on installing the modern Rattle graphical user interface.

Offers a suite of tools designed to enhance the responsiveness and interactivity of web-based documents and applications created with R. It provides an automatic, configurable resizing toolbar that can be seamlessly integrated with HTML elements such as containers, images, and tables, allowing end-users to dynamically adjust their dimensions. Beyond the toolbar, the package includes a rich collection of flexible, expandable, and interactive container functionalities, such as highly customizable split-screen layouts (splitCard), versatile sizeable cards (sizeableCard), dynamic window-like elements (windowCard), visually engaging emphasis cards (empahsisCard), and sophisticated flexible and elastic card layouts (flexCard, elastiCard). Furthermore, it offers an elegant image viewer and resizer (shinyExpandImage) perfect for interactive galleries. r2resize is particularly well-suited for developers and data scientists looking to create modern, responsive, and user-friendly shiny applications, markdown reports, and quarto documents that adapt gracefully to different screen sizes and user preferences, significantly improving the user experience.

Visualize the objects in orbits in 2D and 3D. The packages is under developing to plot the orbits of objects in polar coordinate system. See the examples in demo.

R Commander plugin for teaching statistical methods. It adds a new menu for making easier the teaching of the main concepts about the main statistical methods.

This package provides tools for RFM (recency, frequency and monetary value) analysis. Generate RFM score from both transaction and customer level data. Visualize the relationship between recency, frequency and monetary value using heatmap, histograms, bar charts and scatter plots. Includes a shiny app for interactive segmentation. References: i. Blattberg R.C., Kim BD., Neslin S.A (2008) <doi:10.1007/978-0-387-72579-6_12>.

Biologically relevant, yet mathematically sound constraints are used to compute the propensity and thence infer the dominant direction of reactions of a generic biochemical network. The reactions must be unique and their number must exceed that of the reactants,i.e., reactions >= reactants + 2. ReDirection', computes the null space of a user-defined stoichiometry matrix. The spanning non-zero and unique reaction vectors (RVs) are combinatorially summed to generate one or more subspaces recursively. Every reaction is represented as a sequence of identical components across all RVs of a particular subspace. The terms are evaluated with (biologically relevant bounds, linear maps, tests of convergence, descriptive statistics, vector norms) and the terms are classified into forward-, reverse- and equivalent-subsets. Since, these are mutually exclusive the probability of occurrence is binary (all, 1; none, 0). The combined propensity of a reaction is the p1-norm of the sub-propensities, i.e., sum of the products of the probability and maximum numeric value of a subset (least upper bound, greatest lower bound). This, if strictly positive is the probable rate constant, is used to infer dominant direction and annotate a reaction as "Forward (f)", "Reverse (b)" or "Equivalent (e)". The inherent computational complexity (NP-hard) per iteration suggests that a suitable value for the number of reactions is around 20. Three functions comprise ReDirection. These are check_matrix() and reaction_vector() which are internal, and calculate_reaction_vector() which is external.

This package provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species distributions.

Minirhizotrons are widely used to observe and explore roots and their growth. This package provides the means to stitch images and divide them into depth layers. Please note that this R package was developed alongside the following manuscript: Stitching root scans and extracting depth layer information -- a workflow and practical examples, S. Kersting, L. Knüver, and M. Fischer. The manuscript is currently in preparation and should be citet as soon as it is available. This project was supported by the project ArtIGROW, which is a part of the WIR!-Alliance ArtIFARM â Artificial Intelligence in Farming funded by the German Federal Ministry of Research, Technology and Space (No. 03WIR4805).

Get data from Linkedin Advertising API <https://learn.microsoft.com/en-us/linkedin/marketing/overview?view=li-lms-2023-10>. You can load ad account hierarchy (accounts, users, campaign groups, campaigns and creatives) and also you can load ad analytics data from your Linkedin Ad account.

Fits cause-specific random survival forests for flexible multistate survival analysis with covariate-adjusted transition probabilities computed via product-integral. State transitions are modeled by random forests. Subject-specific transition probability matrices are assembled from predicted cumulative hazards using the product-integral formula. Also provides a standalone Aalen-Johansen nonparametric estimator as a covariate-free baseline. Supports arbitrary state spaces with any number of states (three or more) and any set of allowed transitions, applicable to clinical trials, disease progression, reliability engineering, and other domains where subjects move among discrete states over time. Provides per-transition feature importance, bias-variance diagnostics, and comprehensive visualizations. Handles right censoring and competing transitions. Methods are described in Ishwaran et al. (2008) <doi:10.1214/08-AOAS169> for random survival forests, Putter et al. (2007) <doi:10.1002/sim.2712> for multistate competing risks decomposition, and Aalen and Johansen (1978) <https://www.jstor.org/stable/4615704> for the nonparametric estimator.

Download and open manifest files provided by the Copernicus Global Land Service data <https://land.copernicus.eu/global/>. The manifest files are available at: <https://land.copernicus.vgt.vito.be/manifest/>. Also see: <https://land.copernicus.eu/global/access/>. Before you can download the data, you will first need to register to create a username and password.

The Ryan-Holm step-down Bonferroni or Sidak procedure is to control the family-wise (experiment-wise) type I error rate in the multiple comparisons. This procedure provides the adjusting p-values and adjusting CIs. The methods used in this package are referenced from John Ludbrook (2000) <doi:10.1046/j.1440-1681.2000.03223.x>.

Various tools for handling fuzzy measures, calculating Shapley value and interaction index, Choquet and Sugeno integrals, as well as fitting fuzzy measures to empirical data are provided. Construction of fuzzy measures from empirical data is done by solving a linear programming problem by using lpsolve package, whose source in C adapted to the R environment is included. The description of the basic theory of fuzzy measures is in the manual in the Doc folder in this package. Please refer to the following: [1] <https://personal-sites.deakin.edu.au/~gleb/fmtools.html> [2] G. Beliakov, H. Bustince, T. Calvo, A Practical Guide to Averaging', Springer, (2016, ISBN: 978-3-319-24753-3). [3] G. Beliakov, S. James, J-Z. Wu, Discrete Fuzzy Measures', Springer, (2020, ISBN: 978-3-030-15305-2).

Hydrologic modelling system is an object oriented tool for simulation and analysis of hydrologic events. The package proposes functions and methods for construction, simulation, visualization, and calibration of a hydrologic model.

This package provides methods for Resampling-based False Discovery Proportion control. A function is provided that provides simultaneous, multi-resolution False Discovery Exceedance (FDX) control as described in Hemerik (2025) <doi:10.48550/arXiv.2509.02376>.

PADRINO houses textual representations of Integral Projection Models which can be converted from their table format into full kernels to reproduce or extend an already published analysis. Rpadrino is an R interface to this database. For more information on Integral Projection Models, see Easterling et al. (2000) <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>, and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. See Levin et al. (2021) for more information on ipmr', the engine that powers model reconstruction <doi:10.1111/2041-210X.13683>.

Generates both total- and level-specific R-squared measures from Rights and Sterbaâ s (2019) <doi:10.1037/met0000184> framework of R-squared measures for multilevel models with random intercepts and/or slopes, which is based on a complete decomposition of variance. Additionally generates graphical representations of these R-squared measures to allow visualizing and interpreting all measures in the framework together as an integrated set. This framework subsumes 10 previously-developed R-squared measures for multilevel models as special cases of 5 measures from the framework, and it also includes several newly-developed measures. Measures in the framework can be used to compute R-squared differences when comparing multilevel models (following procedures in Rights & Sterba (2020) <doi:10.1080/00273171.2019.1660605>). Bootstrapped confidence intervals can also be calculated. To use the confidence interval functionality, download bootmlm from <https://github.com/marklhc/bootmlm>.

ROSE (RObust Semiparametric Efficient) random forests for robust semiparametric efficient estimation in partially parametric models (containing generalised partially linear models). Details can be found in the paper by Young and Shah (2024) <doi:10.48550/arXiv.2410.03471>.