This package provides tools for diagnosing the reproducibility of statistical model outputs under data perturbations. Implements bootstrap, subsampling, and noise-based perturbation schemes and computes coefficient stability, p-value stability, selection stability, prediction stability, and a composite reproducibility index on a 0 to 100 scale. Includes cross-validation ranking stability for model comparison and visualization utilities. Optional backends support robust M-estimation ('MASS') and penalized regression ('glmnet'). Bootstrap perturbation follows Efron and Tibshirani (1993, ISBN:9780412042317); selection stability follows Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>; reproducibility framework follows Peng (2011) <doi:10.1126/science.1213847>.

The handling of an API key (misnomer for password) for protected data can be difficult. This package provides secure convenience functions for entering / handling API keys and pulling data directly into memory. By default it will load from REDCap instances, but other sources are injectable via inversion of control.

An implementation of the RainFARM (Rainfall Filtered Autoregressive Model) stochastic precipitation downscaling method (Rebora et al. (2006) <doi:10.1175/JHM517.1>). Adapted for climate downscaling according to D'Onofrio et al. (2018) <doi:10.1175/JHM-D-13-096.1> and for complex topography as in Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>. The RainFARM method is based on the extrapolation to small scales of the Fourier spectrum of a large-scale precipitation field, using a fixed logarithmic slope and random phases at small scales, followed by a nonlinear transformation of the resulting linearly correlated stochastic field. RainFARM allows to generate ensembles of spatially downscaled precipitation fields which conserve precipitation at large scales and whose statistical properties are consistent with the small-scale statistics of observed precipitation, based only on knowledge of the large-scale precipitation field.

This package provides a client library for The Guardian (https://www.guardian.com/) and their API, this package allows users to search for Guardian articles and retrieve both the content and metadata.

This package provides an interface to Blimp software for Bayesian latent variable modeling, missing data analysis, and multiple imputation. The package generates Blimp syntax, executes Blimp models, and imports results back into R as structured objects with methods for visualization and analysis. Requires Blimp software (freely available at <https://www.appliedmissingdata.com/blimp>) to be installed separately.

Enhances the R Optimization Infrastructure ('ROI') package with the SCS solver for solving convex cone problems.

Reversion mutations are secondary mutations that reverse the deleterious effects of an original pathogenic mutation, partially or fully restoring the gene's function. The revert package detects reversion mutations for a specific pathogenic mutation from DNA-seq bam files.

R interface for calling OpenCV routines that works by translating R objects to OpenCV classes and back. Low-level wrappers for several OpenCV routines are provided as Rcpp modules. In addition, high level interfaces are provided for a limited selection of common operations.

Utility functions for interacting with the COMPADRE and COMADRE databases of matrix population models. Described in Jones et al. (2021) <doi:10.1101/2021.04.26.441330>.

Perform wavelet analysis (orthogonal,translation invariant, tensorial, 1-2-3d transforms, thresholding, block thresholding, linear,...) with applications to data compression, denoising/regression or clustering. The core of the code is a port of MATLAB Wavelab toolbox written by D. Donoho, A. Maleki and M. Shahram.

Facilitate data preparation for data collected on WEXTOR <https://wextor.eu>, created by Reips and Neuhaus (2002) <doi:10.3758/bf03195449>. Perform plausibility and other checks and make use of cool color palettes and themes for data visualization.

This package provides a configuration-driven framework for running domain-level data quality checks and consolidating findings into structured Excel reports with role-based feedback routing. It supports trial-level and study-level checks across multiple data domains. Reports are routed to separate feedback channels for Data Management (DM), Medical Writing (MW), Study Data Tabulation Model (SDTM) programmers, and Analysis Data Model (ADaM) programmers, as well as other relevant data roles. Reviewer responses are incorporated automatically on re-run.

Generate causally-simulated data to serve as ground truth for evaluating methods in causal discovery and effect estimation. The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rcausim/blob/master/doc/causal_simulation_exemplar.html>.

This package provides a part of precision agriculture is linked to the spectral image obtained from the cameras. With the image information of the agricultural experiment, the included functions facilitate the collection of spectral data associated with the experimental units. Some designs generated in R are linked to the images, which allows the use of the information of each pixel of the image in the experimental unit and the treatment. Tables and images are generated for the analysis of the precision agriculture experiment during the entire vegetative period of the crop.

Imports data from the SurveyXact commercial service <https://rambollxact.com>, adds variable labels, and converts value labels to factors in the same order as exported from SurveyXact'.

Suite of tools for using D3', a library for producing dynamic, interactive data visualizations. Supports translating objects into D3 friendly data structures, rendering D3 scripts, publishing D3 visualizations, incorporating D3 in R Markdown, creating interactive D3 applications with Shiny, and distributing D3 based htmlwidgets in R packages.

This package contains function rkt which computes the Mann-Kendall test (MK) and the Seasonal and the Regional Kendall Tests for trend (SKT and RKT) and Theil-Sen's slope estimator.

The goal of ralger is to facilitate web scraping in R.

Connects dataframes/tables with a remote data source. Raw data downloaded from the data source can be further processed and transformed using data preparation code that is also baked into the dataframe/table. Refreshable dataframes can be shared easily (e.g. as R data files). Their users do not need to care about the inner workings of the data update mechanisms.

Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with heterogeneous data sets and partially missing data management. It includes models for real, categorical, counting, functional and ranking data. This package contains the minimal R interface of the C++ MixtComp library.

An R package for the OpenSecrets.org web services API.

Visualize networks using the javascript library roughjs'. This allows to draw sketchy, hand-drawn-like networks.

Reconstructs retinae by morphing a flat surface with cuts (a dissected flat-mount retina) onto a curvilinear surface (the standard retinal shape). It can estimate the position of a point on the intact adult retina to within 8 degrees of arc (3.6% of nasotemporal axis). The coordinates in reconstructed retinae can be transformed to visuotopic coordinates. For more details see Sterratt, D. C., Lyngholm, D., Willshaw, D. J. and Thompson, I. D. (2013) <doi:10.1371/journal.pcbi.1002921>.

Applies methods used to estimate animal homerange, but instead of geospatial coordinates, we use isotopic coordinates. The estimation methods include: 1) 2-dimensional bivariate normal kernel utilization density estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon estimator, all applied to stable isotope data. Additionally, functions to determine niche area, polygon overlap between groups and levels (confidence contours) and plotting capabilities.