Compute the R-squared measure under the accelerated failure time (AFT) models proposed in Chan et. al. (2018) <doi:10.1080/03610918.2016.1177072>.

Automatic fixed rank kriging for (irregularly located) spatial data using a class of basis functions with multi-resolution features and ordered in terms of their resolutions. The model parameters are estimated by maximum likelihood (ML) and the number of basis functions is determined by Akaike's information criterion (AIC). For spatial data with either one realization or independent replicates, the ML estimates and AIC are efficiently computed using their closed-form expressions when no missing value occurs. Details regarding the basis function construction, parameter estimation, and AIC calculation can be found in Tzeng and Huang (2018) <doi:10.1080/00401706.2017.1345701>. For data with missing values, the ML estimates are obtained using the expectation- maximization algorithm. Apart from the number of basis functions, there are no other tuning parameters, making the method fully automatic. Users can also include a stationary structure in the spatial covariance, which utilizes LatticeKrig package.

Trigger animation effects on scroll on any HTML element of shiny and rmarkdown', such as any text or plot, thanks to the AOS Animate On Scroll jQuery library.

Predicts antimicrobial peptides using random forests trained on the n-gram encoded peptides. The implemented algorithm can be accessed from both the command line and shiny-based GUI. The AmpGram model is too large for CRAN and it has to be downloaded separately from the repository: <https://github.com/michbur/AmpGramModel>.

Determination of absolute protein quantities is necessary for multiple applications, such as mechanistic modeling of biological systems. Quantitative liquid chromatography tandem mass spectrometry (LC-MS/MS) proteomics can measure relative protein abundance on a system-wide scale. To estimate absolute quantitative information using these relative abundance measurements requires additional information such as heavy-labeled references of known concentration. Multiple methods have been using different references and strategies; some are easily available whereas others require more effort on the users end. Hence, we believe the field might benefit from making some of these methods available under an automated framework, which also facilitates validation of the chosen strategy. We have implemented the most commonly used absolute label-free protein abundance estimation methods for LC-MS/MS modes quantifying on either MS1-, MS2-levels or spectral counts together with validation algorithms to enable automated data analysis and error estimation. Specifically, we used Monte-carlo cross-validation and bootstrapping for model selection and imputation of proteome-wide absolute protein quantity estimation. Our open-source software is written in the statistical programming language R and validated and demonstrated on a synthetic sample.

Nonparametric data-driven approach to discovering heterogeneous subgroups in a selection-on-observables framework. aggTrees allows researchers to assess whether there exists relevant heterogeneity in treatment effects by generating a sequence of optimal groupings, one for each level of granularity. For each grouping, we obtain point estimation and inference about the group average treatment effects. Please reference the use as Di Francesco (2022) <doi:10.2139/ssrn.4304256>.

This package provides a modeling package compiling applicability domain methods in R. It combines different methods to measure the amount of extrapolation new samples can have from the training set. See <doi:10.4018/IJQSPR.2016010102> for an overview of applicability domains.

Visualize results generated by Antares, a powerful open source software developed by RTE to simulate and study electric power systems (more information about Antares here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). This package provides functions that create interactive charts to help Antares users visually explore the results of their simulations.

This package provides functions to accompany the book "Applied Statistical Modeling for Ecologists" by Marc Kéry and Kenneth F. Kellner (2024, ISBN: 9780443137150). Included are functions for simulating and customizing the datasets used for the example models in each chapter, summarizing output from model fitting engines, and running custom Markov Chain Monte Carlo.

Estimates the attributable fraction in different sampling designs adjusted for measured confounders using logistic regression (cross-sectional and case-control designs), conditional logistic regression (matched case-control design), Cox proportional hazard regression (cohort design with time-to- event outcome), gamma-frailty model with a Weibull baseline hazard and instrumental variables analysis. An exploration of the AF with a genetic exposure can be found in the package AFheritability Dahlqwist E et al. (2019) <doi:10.1007/s00439-019-02006-8>.

This package implements a Bayesian adaptive graphical lasso data-augmented block Gibbs sampler. The sampler simulates the posterior distribution of precision matrices of a Gaussian Graphical Model. This sampler was adapted from the original MATLAB routine proposed in Wang (2012) <doi:10.1214/12-BA729>.

This package provides a production-grade AI toolkit for R featuring a layered architecture (Specification, Utilities, Providers, Core), request interception support, robust error handling with exponential retry delays, support for multiple AI model providers ('OpenAI', Anthropic', etc.), local small language model inference, distributed MCP ecosystem, multi-agent orchestration, progressive knowledge loading through skills, and a global skill store for sharing AI capabilities.

Terrain change detection, cut and fill volume estimation, terrain profiling, reclamation monitoring, erosion analysis, and flood risk assessment from LiDAR (Light Detection and Ranging) point clouds and digital elevation models ('DEMs'). Applications include surface mine reclamation monitoring, sediment pond capacity tracking, highwall safety classification, and erosion channel detection. Built on lidR for point cloud I/O and terra for raster operations. Includes access utilities for KyFromAbove cloud-native elevation data on Amazon Web Services ('AWS') <https://kyfromabove.ky.gov/>. Methods for terrain change detection and volume estimation follow Li and others (2005) <doi:10.1016/j.geomorph.2004.10.007>.

The tools in this package are intended to help researchers assess multiple treatment-covariate interactions with data from a parallel-group randomized controlled clinical trial. The methods implemented in the package were proposed in Kovalchik, Varadhan and Weiss (2013) <doi: 10.1002/sim.5881>.

This toolkit implements a numerical solution algorithm to invert a quality of life measure from observed data. Unlike the traditional Rosen-Roback measure, this measure accounts for mobility frictionsâ generated by idiosyncratic tastes and local ties â and trade frictions â generated by trade costs and non-tradable services, thereby reducing non-classical measurement error. The QoL measure is based on Ahlfeldt, Bald, Roth, Seidel (2024) <https://econpapers.repec.org/RePEc:boc:bocode:s459382> "Measuring Quality of Life under Spatial Frictions". When using this programme or the toolkit in your work, please cite the paper.

This package provides a chat package connecting to API endpoints by OpenAI (<https://platform.openai.com/>) to answer questions (about R).

An interface to the AutoDesk API Platform including the Authentication API for obtaining authentication to the AutoDesk Forge Platform, Data Management API for managing data across the platform's cloud services, Design Automation API for performing automated tasks on design files in the cloud, Model Derivative API for translating design files into different formats, sending them to the viewer app, and extracting design data, and Viewer for rendering 2D and 3D models.

Many complex plots are actually composite plots, such as oncoplot', funkyheatmap', upsetplot', etc. We can produce subplots using ggplot2 and combine them to create composite plots using aplot'. In this way, it is easy to customize these complex plots, by adding, deleting or modifying subplots in the final plot. This package provides a set of utilities to help users to create subplots and complex plots.

Bayesian inference using the no-U-turn (NUTS) algorithm by Hoffman and Gelman (2014) <https://www.jmlr.org/papers/v15/hoffman14a.html>. Designed for AD Model Builder ('ADMB') models, or when R functions for log-density and log-density gradient are available, such as Template Model Builder models and other special cases. Functionality is similar to Stan', and the rstan and shinystan packages are used for diagnostics and inference.

Collect your data on digital marketing campaigns from Adform Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package provides a dependency-free collection of simple functions for cleaning rectangular data. This package allows to detect, count and replace values or discard rows/columns using a predicate function. In addition, it provides tools to check conditions and return informative error messages.

Generate code for use with the Optical Mark Recognition free software Auto Multiple Choice (AMC). More specifically, this package provides functions that use as input the question and answer texts, and output the LaTeX code for AMC.

Estimation and inference methods for bounding average treatment effects (on the treated) that are valid under an unconfoundedness assumption. The bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. This robustness is achieved by only using limited "pooling" of information across observations. For more details, see the paper by Lee and Weidner (2021), "Bounding Treatment Effects by Pooling Limited Information across Observations," <arXiv:2111.05243>.

Edit an Antares simulation before running it : create new areas, links, thermal clusters or binding constraints or edit existing ones. Update Antares general & optimization settings. Antares is an open source power system generator, more information available here : <https://antares-simulator.org/>.