Allows access to data in running instance of Microsoft Excel (e. g. xl[a1] = xl[b2]*3 and so on). Graphics can be transferred with xl[a1] = current.graphics()'. Additionally there are function for reading/writing Excel files - xl.read.file'/'xl.save.file'. They are not fast but able to read/write *.xlsb'-files and password-protected files. There is an Excel workbook with examples of calling R from Excel in the doc folder. It tries to keep things as simple as possible - there are no needs in any additional installations besides R, only VBA code in the Excel workbook. Microsoft Excel is required for this package.

Support functions for R-based "EQUALCompareImages - Compare similarity between and within images" shiny application which allow researchers without coding skills or expertise in image comparison algorithms to compare images. Gurusamy,K (2025)<doi:10.5281/zenodo.16994047>.

An implementation of Bayesian hierarchical models for faecal egg count data to assess anthelmintic efficacy. Bayesian inference is done via MCMC sampling using Stan <https://mc-stan.org/>.

This package provides step-by-step automation for integrating biodiversity data from multiple online aggregators, merging and cleaning datasets while addressing challenges such as taxonomic inconsistencies, georeferencing issues, and spatial or environmental outliers. Includes functions to extract environmental data and to define the biogeographic ranges in which species are most likely to occur. For methodological details see the associated publication.<doi: 10.1002/ecog.08203>.

Analysis of elliptical tubes with applications in biological modeling. The package is based on the references: Taheri, M., Pizer, S. M., & Schulz, J. (2024) "The Mean Shape under the Relative Curvature Condition." Journal of Computational and Graphical Statistics <doi:10.1080/10618600.2025.2535600> and arXiv <doi:10.48550/arXiv.2404.01043>. Mohsen Taheri Shalmani (2024) "Shape Statistics via Skeletal Structures", PhD Thesis, University of Stavanger, Norway <doi:10.13140/RG.2.2.34500.23685>. Key features include constructing discrete elliptical tubes, calculating transformations, validating structures under the Relative Curvature Condition (RCC), computing means, and generating simulations. Supports intrinsic and non-intrinsic mean calculations and transformations, size estimation, plotting, and random sample generation based on a reference tube. The intrinsic approach relies on the interior path of the original non-convex space, incorporating the RCC, while the non-intrinsic approach uses a basic robotic arm transformation that disregards the RCC.

This package contains utilities for the analysis of protein sequences in a phylogenetic context. Allows the generation of phylogenetic trees base on protein sequences in an alignment-independent way. Two different methods have been implemented. One approach is based on the frequency analysis of n-grams, previously described in Stuart et al. (2002) <doi:10.1093/bioinformatics/18.1.100>. The other approach is based on the species-specific neighborhood preference around amino acids. Features include the conversion of a protein set into a vector reflecting these neighborhood preferences, pairwise distances (dissimilarity) between these vectors, and the generation of trees based on these distance matrices.

This package provides a small set of functions for managing R environments, with defaults designed to encourage usage patterns that scale well to larger code bases. It provides: import_from(), a flexible way to assign bindings that defaults to the current environment; include(), a vectorized alternative to base::source() that also default to the current environment; and attach_eval() and attach_source(), a way to evaluate expressions in attached environments. Together, these (and other) functions pair to provide a robust alternative to base::library() and base::source().

This package provides utility functions for standardizing economic entity (economy, aggregate, institution, etc.) name and id in economic datasets such as those published by the International Monetary Fund and World Bank. Aims to facilitate consistent data analysis, reporting, and joining across datasets. Used as a foundational building block in the EconDataverse family of packages (<https://www.econdataverse.org>).

This package provides a collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.

Computes the top-k singular triplets or eigenpairs of large sparse and structured matrices: the computation behind principal component analysis on big sparse data, spectral embeddings, and low-rank approximation. Every result carries a numerical certificate with residuals, a backward-error bound, orthogonality loss, and a pass/fail flag, and bounds that can only be estimated are reported as such rather than passed. Centered, scaled, and composed operators are solved through native C++ kernels without forming dense matrices. Drop-in replacements for the RSpectra interface are included.

Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.

This package creates simple to highly customized tables for a wide selection of descriptive statistics, with or without weighting the data.

Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Fox M.P., MacLehose R.F., and Lash T.L. "Applying Quantitative Bias Analysis to Epidemiologic Data, second ed.", ('Springer', 2021).

This package implements an explicit exploration strategy for evolutionary algorithms in order to have a more effective search in solving optimization problems. Along with this exploration search strategy, a set of four different Estimation of Distribution Algorithms (EDAs) are also implemented for solving optimization problems in continuous domains. The implemented explicit exploration strategy in this package is described in Salinas-Gutiérrez and Muñoz Zavala (2023) <doi:10.1016/j.asoc.2023.110230>.

This package performs some enhanced variable selection algorithms based on the least absolute shrinkage and selection operator for regression model.

This package provides functions for age standardisation of epidemiological measures such as incidence and prevalence rates. It allows users to apply standard population structures to observed age-specific estimates in order to obtain comparable summary measures across populations or time periods. Functions support calculation of standardised rates, outcome counts, and corresponding confidence intervals. The tools are designed to facilitate reproducible and transparent adjustment for differences in age distributions in epidemiological and public health research.

This package implements the Bayesian Exchangeable Non-Exchangeable (EXNEX) framework for right-censored log-normal survival data in basket trials. Based on Rcpp and RcppArmadillo', the package provides a fast Gibbs sampler supporting EXNEX, complete pooling, and no pooling models to facilitate methodological comparisons and simulation studies.

Expert Algorithm Verbal Autopsy assigns causes of death to 2016 WHO Verbal Autopsy Questionnaire data. odk2EAVA() converts data to a standard input format for cause of death determination building on the work of Thomas (2021) <https://cran.r-project.org/src/contrib/Archive/CrossVA/>. codEAVA() uses the presence and absence of signs and symptoms reported in the Verbal Autopsy interview to diagnose common causes of death. A deterministic algorithm assigns a single cause of death to each Verbal Autopsy interview record using a hierarchy of all common causes for neonates or children 1 to 59 months of age.

Two methods for performing equivalence test for the means of two (test and reference) normal distributions are implemented. The null hypothesis of the equivalence test is that the absolute difference between the two means are greater than or equal to the equivalence margin and the alternative is that the absolute difference is less than the margin. Given that the margin is often difficult to obtain a priori, it is assumed to be a constant multiple of the standard deviation of the reference distribution. The first method assumes a fixed margin which is a constant multiple of the estimated standard deviation of the reference data and whose variability is ignored. The second method takes into account the margin variability. In addition, some tools to summarize and illustrate the data and test results are included to facilitate the evaluation of the data and interpretation of the results.

Allows the user to determine minimum sample sizes that achieve target size and power at a specified alternative. For more information, see â Exact samples sizes for clinical trials subject to size and power constraintsâ by Lloyd, C.J. (2022) Preprint <doi:10.13140/RG.2.2.11828.94085>.

Routines for epidemiological contact tracing and visualisation of network of contacts.

Forecasting time series with different decomposition based ARIMA models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>.

The purpose of Early Warning Systems (EWS) is to detect accurately the occurrence of a crisis, which is represented by a binary variable which takes the value of one when the event occurs, and the value of zero otherwise. EWS are a toolbox for policymakers to prevent or attenuate the impact of economic downturns. Modern EWS are based on the econometric framework of Kauppi and Saikkonen (2008) <doi:10.1162/rest.90.4.777>. Specifically, this framework includes four dichotomous models, relying on a logit approach to model the relationship between yield spreads and future recessions, controlling for recession risk factors. These models can be estimated in a univariate or a balanced panel framework as in Candelon, Dumitrescu and Hurlin (2014) <doi:10.1016/j.ijforecast.2014.03.015>. This package provides both methods for estimating these models and a dataset covering 13 OECD countries over a period of 45 years. In addition, this package also provides methods for the analysis of the propagation mechanisms of an exogenous shock, as well as robust confidence intervals for these response functions using a block-bootstrap method as in Lajaunie (2021). This package constitutes a useful toolbox (data and functions) for scholars as well as policymakers.

Biotracers and stomach content analyses are combined in a Bayesian hierarchical model to estimate a probabilistic topology matrix (all trophic link probabilities) and a diet matrix (all diet proportions). The package relies on the JAGS software and the jagsUI package to run a Markov chain Monte Carlo approximation of the different variables.