This package provides functions to calculate the normalised Lineage-Through- Time (nLTT) statistic, given two phylogenetic trees. The nLTT statistic measures the difference between two Lineage-Through-Time curves, where each curve is normalised both in time and in number of lineages.

An interface between R and the OSRM API. OSRM is a routing service based on OpenStreetMap data. See <http://project-osrm.org/> for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometric distance).

The goal of stim is to provide a function for estimating the Stability Informed Model. The Stability Informed Model integrates stability information (how much a variable correlates with itself in the future) into cross-sectional estimates. Wysocki and Rhemtulla (2022) <https://psyarxiv.com/vg5as>.

Alternative to using withCallingHandlers() in the simple case of catch and rethrow. The `%!%` operator evaluates the expression on its left hand side, and if an error occurs, the right hand side is used to construct a new error that embeds the original error.

Generates data from R or JAGS code for use in simulation studies. The data are returned as an nlist::nlists object and/or saved to file as individual .rds files. Parallelization is implemented using the future package. Progress is reported using the progressr package.

This package implements the Bayesian model selection method with suspected latent grouping factor methodology of Metzger and Franck (2020), <doi:10.1080/00401706.2020.1739561>. SLGF detects latent heteroscedasticity or group-based regression effects based on the levels of a user-specified categorical predictor.

This package provides a tufte'-alike style for rmarkdown'. A modern take on the Tufte design for pdf and html vignettes, building on the tufte package with additional contributions from the knitr and ggtufte package, and also acknowledging the key influence of envisioned css'.

We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998617749561>.

Native R interface to TMB (Template Model Builder) so models can be written entirely in R rather than C++'. Automatic differentiation, to any order, is available for a rich subset of R features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. RTMB provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.

This package provides tools for accessing the Botanical Information and Ecology Network (BIEN) database. The BIEN database contains cleaned and standardized botanical data including occurrence, trait, plot and taxonomic data. This package provides functions that query the BIEN database by constructing and executing optimized SQL queries.

The aim of httr is to provide a wrapper for RCurl customised to the demands of modern web APIs. It provides useful tools for working with HTTP organised by HTTP verbs (GET(), POST(), etc). Configuration functions make it easy to control additional request components.

This package provides tools supporting multi-criteria and group decision making, including variable number of criteria, by means of aggregation operators, spread measures, fuzzy logic connectives, fusion functions, and preordered sets. Possible applications include, but are not limited to, quality management, scientometrics, software engineering, etc.

This package implements methods for building and analyzing models based on panel data as described in the paper by Moral-Benito (2013, <doi:10.1080/07350015.2013.818003>). The package provides functions to estimate dynamic panel data models and analyze the results of the estimation.

Build decision trees and random forests for classification and regression. The implementation strikes a balance between minimizing computing efforts and maximizing the expected predictive accuracy, thus scales well to large data sets. Multi-threading is available through OpenMP <https://gcc.gnu.org/wiki/openmp>.

Generates all necessary C functions allowing the user to work with the compiled-code interface of ode() and bvptwp(). The implementation supports "forcings" and "events". Also provides functions to symbolically compute Jacobians, sensitivity equations and adjoint sensitivities being the basis for sensitivity analysis.

This function conducts the Cochran-Armitage trend test to a 2 by k contingency table. It will report the test statistic (Z) and p-value.A linear trend in the frequencies will be calculated, because the weights (0,1,2) will be used by default.

Estimates the causal decompositions of group disparities developed by Yu and Elwert (2025) <doi:10.1214/24-AOAS1990>. For the nuisance functions of the estimators, we provide both parametric and nonparametric options, as well as manual options in case the default models are not satisfying.

Estimates latent variables of public opinion cross-nationally and over time from sparse and incomparable survey data. DCPO uses a population-level graded response model with country-specific item bias terms. Sampling is conducted with Stan'. References: Solt (2020) <doi:10.31235/osf.io/d5n9p>.

This package provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models. As a special case of the EBglmnet package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.

This package provides a methodology simple and trustworthy for the analysis of extreme values and multiple threshold tests for a generalized Pareto distribution, together with an automatic threshold selection algorithm. See del Castillo, J, Daoudi, J and Lockhart, R (2014) <doi:10.1111/sjos.12037>.

This package provides a collection of functions for testing randomness (or mutual independence) in linear and circular data as proposed in Gehlot and Laha (2025a) <doi:10.48550/arXiv.2506.21157> and Gehlot and Laha (2025b) <doi:10.48550/arXiv.2506.23522>, respectively.

Generates experiments - simulating structured or experimental data as: completely randomized design, randomized block design, latin square design, factorial and split-plot experiments (Ferreira, 2008, ISBN:8587692526; Naes et al., 2007 <doi:10.1002/qre.841>; Rencher et al., 2007, ISBN:9780471754985; Montgomery, 2001, ISBN:0471316490).

Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood (see Albert, C., Vogel, S. and Ashauer, R. (2016) <doi:10.1371/journal.pcbi.1004978>).

Set of R functions to be coupled with the xeus-r jupyter kernel in order to drive execution of code in notebook input cells, how R objects are to be displayed in output cells, and handle two way communication with the front end through comms.