Amends errors, augments data and aids analysis of John Snow's map of the 1854 London cholera outbreak.

This package provides composable optimization strategies for maximum likelihood estimation (MLE). Solvers are first-class functions that combine via sequential chaining, parallel racing, and random restarts. Implements gradient ascent, Newton-Raphson, quasi-Newton (BFGS), and derivative-free methods with support for constrained optimization and tracing. Returns mle objects compatible with algebraic.mle for downstream analysis. Methods based on Nocedal J, Wright SJ (2006) "Numerical Optimization" <doi:10.1007/978-0-387-40065-5>.

When causal quantities are not identifiable from the observed data, it still may be possible to bound these quantities using the observed data. We outline a class of problems for which the derivation of tight bounds is always a linear programming problem and can therefore, at least theoretically, be solved using a symbolic linear optimizer. We extend and generalize the approach of Balke and Pearl (1994) <doi:10.1016/B978-1-55860-332-5.50011-0> and we provide a user friendly graphical interface for setting up such problems via directed acyclic graphs (DAG), which only allow for problems within this class to be depicted. The user can then define linear constraints to further refine their assumptions to meet their specific problem, and then specify a causal query using a text interface. The program converts this user defined DAG, query, and constraints, and returns tight bounds. The bounds can be converted to R functions to evaluate them for specific datasets, and to latex code for publication. The methods and proofs of tightness and validity of the bounds are described in a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) <doi:10.1080/10618600.2022.2071905>.

Helps users standardise data to the Darwin Core Standard, a global data standard to store, document, and share biodiversity data like species occurrence records. The package provides tools to manipulate data to conform with, and check validity against, the Darwin Core Standard. Using corella allows users to verify that their data can be used to build Darwin Core Archives using the galaxias package.

Perform likelihood estimation and corresponding analysis under the copula-based Markov chain model for serially dependent event times with a dependent terminal event. Available are statistical methods in Huang, Wang and Emura (2020, JJSD accepted).

Fast and user-friendly estimation of generalized linear models with multiple fixed effects and cluster the standard errors. The method to obtain the estimated fixed-effects coefficients is based on Stammann (2018) <doi:10.48550/arXiv.1707.01815>, Gaure (2013) <doi:10.1016/j.csda.2013.03.024>, Berge (2018) <https://ideas.repec.org/p/luc/wpaper/18-13.html>, and Correia et al. (2020) <doi: 10.1177/1536867X20909691>. This implementation is described in Vargas Sepulveda (2025) <doi:10.1371/journal.pone.0331178>.

This calculates a variety of different CIs for proportions and difference of proportions that are commonly used in the pharmaceutical industry including Wald, Wilson, Clopper-Pearson, Agresti-Coull and Jeffreys for proportions. And Miettinen-Nurminen (1985) <doi:10.1002/sim.4780040211>, Wald, Haldane, and Mee <https://www.lexjansen.com/wuss/2016/127_Final_Paper_PDF.pdf> for difference in proportions.

The Certifiably Optimal RulE ListS (Corels) learner by Angelino et al described in <doi:10.48550/arXiv.1704.01701> provides interpretable decision rules with an optimality guarantee, and is made available to R with this package. See the file AUTHORS for a list of copyright holders and contributors.

Perform sparse estimation of a Gaussian graphical model (GGM) with node aggregation through variable clustering. Currently, the package implements the clusterpath estimator of the Gaussian graphical model (CGGM) (Touw, Alfons, Groenen & Wilms, 2025; <doi:10.48550/arXiv.2407.00644>).

Wraps the CIRCE (<https://github.com/ohdsi/circe-be>) Java library allowing cohort definition expressions to be edited and converted to Markdown or SQL'.

This package provides a clinical significance analysis can be used to determine if an intervention has a meaningful or practical effect for patients. You provide a tidy data set plus a few more metrics and this package will take care of it to make your results publication ready. Accompanying package to Claus et al. <doi:10.18637/jss.v111.i01>.

Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) <doi:10.3389/fmicb.2023.1170391>.

Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.

The main function calculates confidence intervals (CI) for Mixed Models, utilizing both classical estimators from the lmer() function in the lme4 package and robust estimators from the rlmer() function in the robustlmm package, as well as the varComprob() function in the robustvarComp package. Three methods are available: the classical Wald method, the wild bootstrap, and the parametric bootstrap. Bootstrap methods offer flexibility in obtaining lower and upper bounds through percentile or BCa methods. More details are given in Mason, F., Cantoni, E., & Ghisletta, P. (2021) <doi:10.5964/meth.6607> and Mason, F., Cantoni, E., & Ghisletta, P. (2024) <doi:10.1037/met0000643>.

This package contains functions for estimating generalized parametric mixture and non-mixture cure models <doi:10.1016/j.cmpb.2022.107125>, loss of lifetime, mean residual lifetime, and crude event probabilities.

Computes community climate statistics for volume and mismatch using species climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage.

Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>, statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>, Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al (2019)<DOI:10.1016/j.cmpb.2018.10.020>. Algorithms for generating correlated gene expressions are also available. Estimation of survival functions via copula-graphic (CG) estimators is also implemented, which is useful for sensitivity analyses under dependent censoring (Yeh et al 2023 Biomedicines) <DOI:10.3390/biomedicines11030797> and factorial survival analyses (Emura et al 2024 Stat Methods Med Res) <DOI:10.1177/09622802231215805>.

This package performs simple correspondence analysis on a two-way contingency table, or multiple correspondence analysis (homogeneity analysis) on data with p categorical variables, and produces bootstrap-based elliptical confidence regions around the projected coordinates for the category points. Includes routines to plot the results in a variety of styles. Also reports the standard numerical output for correspondence analysis.

This package provides functions to compute tree crown volume and projected area using geometric solid approximations (ellipsoid, cone, cylinder, paraboloid, and fan shapes), and to calculate morphometric indices commonly used in forest inventory and silviculture, including crown ratio, crown form factor, slenderness, salience index, and scope index. Methods are based on peer-reviewed literature in forest science.

Implementations of recent complex-valued wavelet shrinkage procedures for smoothing irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.

Salmonella enterica is a major cause of bacterial food-borne disease worldwide. Serotype identification is the most commonly used typing method to characterize Salmonella isolates. However, experimental serotyping needs great cost on manpower and resources. Recently, we found that the newly incorporated spacer in the clustered regularly interspaced short palindromic repeat (CRISPR) could serve as an effective marker for typing of Salmonella. It was further revealed by Li et. al (2014) <doi:10.1128/JCM.00696-14> that recognized types based on the combination of two newly incorporated spacer in both CRISPR loci showed high accordance with serotypes. Here, we developed an R package CSESA to predict the serotype based on this finding. Considering itâ s time saving and of high accuracy, we recommend to predict the serotypes of unknown Salmonella isolates using CSESA before doing the traditional serotyping.

This package provides a reliable and efficient tool for cleaning univariate time series data. It implements reliable and efficient procedures for automating the process of cleaning univariate time series data. The package provides integration with already developed and deployed tools for missing value imputation and outlier detection. It also provides a way of visualizing large time-series data in different resolutions.

This package provides tools for interpretable conditional forecasting and scenario analysis in reduced-form vector autoregressive (VAR) models. Implements a Kalman smoothing framework to generate forecasts under path restrictions on selected variables. The package enables decomposition of conditional forecasts into variable-specific contributions, and extraction of observation weights. It also computes measures of overall and marginal variable importance to enhance the economic interpretation of forecast revisions. The framework is structurally agnostic and suited for policy analysis, stress testing, and macro-financial applications. The methodology is described in more detail in Caspi and Ginker (2026) <doi:10.13140/RG.2.2.25225.51040>.

Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.