Implement tableGrob object as a clickable image map. The clickableImageMap package is designed to be more convenient and more configurable than the edit() function. Limitations that I have encountered with edit() are cannot control (1) positioning (2) size (3) appearance and formatting of fonts In contrast, when the table is implemented as a tableGrob', all of these features are controllable. In particular, the ggplot2 grid system allows exact positioning of the table relative to other graphics etc.

This package provides a Bayesian method for Phenome-wide association studies (PheWAS) that identifies causal associations between genetic variants and traits, while simultaneously addressing confounding due to linkage disequilibrium. For details see Manipur et al (2024, Nature Communications) <doi:10.1038/s41467-024-49990-8>.

Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) <doi:10.1007/s11222-006-9014-7> which relies on the algorithm of Carpaneto and Toth (1980) <doi:10.1145/355873.355883>.

This package provides a genome-wide survival framework that integrates sequential conditional independent tuples and saddlepoint approximation method, to provide SNP-level false discovery rate control while improving power, particularly for biobank-scale survival analyses with low event rates. The method is based on model-X knockoffs as described in Barber and Candes (2015) <doi:10.1214/15-AOS1337> and fast survival analysis methods from Bi et al. (2020) <doi:10.1016/j.ajhg.2020.06.003>. A shrinkage algorithmic leveraging accelerates multiple knockoffs generation in large genetic cohorts. This CRAN version uses standard Cox regression for association testing. For enhanced performance on very large datasets, users may optionally install the SPACox package from GitHub which provides saddlepoint approximation methods for survival analysis.

This package provides methods to help selecting General Circulation Models (GCMs) in the context of projecting models to future scenarios. It is provided clusterization algorithms, distance and correlation metrics, as well as a tailor-made algorithm to detect the optimum subset of GCMs that recreate the environment of all GCMs as proposed in Esser et al. (2025) <doi:10.1111/gcb.70008>.

This package provides tools for working with the International Classification of Diseases ('ICD-10 Chile official MINSAL'/'DEIS v2018). Includes optimized SQL search with SQLite', fuzzy matching of medical terms (Jaro-Winkler), Charlson and Elixhauser comorbidity calculation, WHO ICD-11 API integration, and hierarchical code validation. Data from Centro FIC Chile DEIS <https://deis.minsal.cl/centrofic/>.

This package provides functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments. A description of the methods is given in Galagate (2016) <https://drum.lib.umd.edu/handle/1903/18170>.

Jointly model the accuracy of cognitive responses and item choices within a Bayesian hierarchical framework as described by Culpepper and Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package contains the datasets used within the analysis of the paper.

This is an opinionated wrapper around the python-chess package. It allows users to read and write PGN files as well as create and explore game trees such as the ones seen in chess books.

Find the location of the code for an R package based on the package's name or string representation. Checks on CRAN based on information in the URL field or BioConductor and GitHub based on constructing a URL, and verifies all paths via testing for a successful response. This can be useful when automating static code analysis based on a list of package names, and similar tasks.

This package provides a tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.

The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight National Meteorological and Hydrological Services. The cmsafops R-package provides a collection of R-operators for the analysis and manipulation of CM SAF NetCDF formatted data. Other CF conform NetCDF data with time, longitude and latitude dimension should be applicable, but there is no guarantee for an error-free application. CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>). Detailed information and test data are provided on the CM SAF webpage (<http://www.cmsaf.eu/R_toolbox>).

Computed tomography (CT) imaging is a powerful tool for understanding the composition of sediment cores. This package streamlines and accelerates the analysis of CT data generated in the context of environmental science. Included are tools for processing raw DICOM images to characterize sediment composition (sand, peat, etc.). Root analyses are also enabled, including measures of external surface area and volumes for user-defined root size classes. For a detailed description of the application of computed tomography imaging for sediment characterization, see: Davey, E., C. Wigand, R. Johnson, K. Sundberg, J. Morris, and C. Roman. (2011) <DOI: 10.1890/10-2037.1>.

Convert MacArthur-Bates Communicative Development Inventory Words and Gestures scores to would-be scores on Words and Sentences, based on modeling from the Stanford Wordbank <https://wordbank.stanford.edu/>. See Day et al. (2025) <doi:10.1111/desc.70036>.

Collection of routines for efficient scientific computations in physics and astrophysics. These routines include utility functions, numerical computation tools, as well as visualisation tools. They can be used, for example, for generating random numbers from spherical and custom distributions, information and entropy analysis, special Fourier transforms, two-point correlation estimation (e.g. as in Landy & Szalay (1993) <doi:10.1086/172900>), binning & gridding of point sets, 2D interpolation, Monte Carlo integration, vector arithmetic and coordinate transformations. Also included is a non-exhaustive list of important constants and cosmological conversion functions. The graphics routines can be used to produce and export publication-ready scientific plots and movies, e.g. as used in Obreschkow et al. (2020, MNRAS Vol 493, Issue 3, Pages 4551â 4569). These routines include special color scales, projection functions, and bitmap handling routines.

This package implements cross-validation methods for linear and ridge regression models. The package provides grid-based selection of the ridge penalty parameter using Singular Value Decomposition (SVD) and supports K-fold cross-validation, Leave-One-Out Cross-Validation (LOOCV), and Generalized Cross-Validation (GCV). Computations are implemented in C++ via RcppArmadillo with optional parallelization using RcppParallel'. The methods are suitable for high-dimensional settings where the number of predictors exceeds the number of observations.

This package provides a finite mixture of Zero-Inflated Poisson (ZIP) models for analyzing criminal trajectories.

Wrapper around the Canadian Mortgage and Housing Corporation (CMHC) web interface. It enables programmatic and reproducible access to a wide variety of housing data from CMHC.

Set of functions for the easy analyses of conditioning data.

The cgAUC can calculate the AUC-type measure of Obuchowski(2006) when gold standard is continuous, and find the optimal linear combination of variables with respect to this measure.

This package provides several functions to identify and analyse miRNA sponge, including popular methods for identifying miRNA sponge interactions, two types of global ceRNA regulation prediction methods and four types of context-specific prediction methods( Li Y et al.(2017) <doi:10.1093/bib/bbx137>), which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. In addition, For predictive ceRNA relationship pairs, this package provides several downstream analysis algorithms, including regulatory network analysis and functional annotation analysis, as well as survival prognosis analysis based on expression of ceRNA ternary pair.

This package provides functions for the estimation of conditional copulas models, various estimators of conditional Kendall's tau (proposed in Derumigny and Fermanian (2019a, 2019b, 2020) <doi:10.1515/demo-2019-0016>, <doi:10.1016/j.csda.2019.01.013>, <doi:10.1016/j.jmva.2020.104610>), test procedures for the simplifying assumption (proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011> and Derumigny, Fermanian and Min (2022) <doi:10.1002/cjs.11742>), and measures of non-simplifyingness (proposed in Derumigny (2025) <doi:10.48550/arXiv.2504.07704>).

This package provides estimates of several summary measures for clinical trials including the average hazard ratio, the weighted average hazard ratio, the restricted superiority probability ratio, the restricted mean survival difference and the ratio of restricted mean times lost, based on the short-term and long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) which accommodates various non-proportional hazards scenarios. The inference procedures and the asymptotic results for the summary measures are discussed in Yang (2018, <doi:10.1002/sim.7676>).

In statistical modeling, multiple models need to be compared based on certain criteria. The method described here uses eight metrics from AllMetrics package. â input_dfâ is the data frame (at least two columns for comparison) containing metrics values in different rows of a column (which denotes a particular modelâ s performance). First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as â MINâ and other values are denoted as â NAâ . Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as â MAXâ and other values are denoted as â NAâ . â output_dfâ contains the similar number of rows (which is 8) and columns (which is number of models to be compared) as of â input_dfâ . Values in â output_dfâ are corresponding â NAâ , â MINâ or â MAXâ . Finally, the column containing minimum number of â NAâ values is denoted as the best column. â min_NA_colâ gives the name of the best column (model). â min_NA_valuesâ are the corresponding metrics values. âBestColumn_metricsâ is the data frame (dimension: 1*8) containing different metrics of the best column (model). â best_column_resultsâ is the final result (a list) containing all of these output elements. In special case, if two columns having equal NA', it will be checked among these two column which one is having least NA in first five rows and will be inferred as the best. More details about AllMetrics can be found in Garai (2023) <doi:10.13140/RG.2.2.18688.30723>.