This package performs automated morphological character partitioning for phylogenetic analyses and analyze macroevolutionary parameter outputs from clock (time-calibrated) Bayesian inference analyses, following concepts introduced by Simões and Pierce (2021) <doi:10.1038/s41559-021-01532-x>.

This package provides tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR.

Support ecological analyses such as ordination and clustering. Contains consistent and easy wrapper functions of stat', vegan', and labdsv packages, and visualisation functions of ordination and clustering.

Life Table Response Experiments (LTREs) are a method of comparative demographic analysis. The purpose is to quantify how the difference or variance in vital rates (stage-specific survival, growth, and fertility) among populations contributes to difference or variance in the population growth rate, "lambda." We provide functions for one-way fixed design and random design LTRE, using either the classical methods that have been in use for several decades, or an fANOVA-based exact method that directly calculates the impact on lambda of changes in matrix elements, for matrix elements and their interactions. The equations and descriptions for the classical methods of LTRE analysis can be found in Caswell (2001, ISBN: 0878930965), and the fANOVA-based exact methods are described in Hernandez et al. (2023) <doi:10.1111/2041-210X.14065>. We also provide some demographic functions, including generation time from Bienvenu and Legendre (2015) <doi:10.1086/681104>. For implementation of exactLTRE where all possible interactions are calculated, we use an operator matrix presented in Poelwijk, Krishna, and Ranganathan (2016) <doi:10.1371/journal.pcbi.1004771>.

This package provides a comprehensive collection of utility functions for data analysis and visualization in R. The package provides 55+ functions for data manipulation, file handling, color palette management, bioinformatics workflows, plotting, and package management. Features include void value handling, custom infix operators, flexible file I/O, and publication-ready visualizations with sensible defaults. Implementation follows tidyverse principles (Wickham et al. (2019) <doi:10.21105/joss.01686>) and incorporates best practices from the R community.

This package contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.

Analyses EuFMDiS output files in a Shiny App. The distributions of relevant output parameters are described in form of tables (quantiles) and plots. The App is called using eufmdis.adapt::run_adapt().

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>.

Allows for forward-in-time simulation of epistatic networks with associated phenotypic output.

This package provides a dataframe-friendly implementation of ComBat Harmonization which uses an empirical Bayesian framework to remove batch effects. Johnson WE & Li C (2007) <doi:10.1093/biostatistics/kxj037> "Adjusting batch effects in microarray expression data using empirical Bayes methods." Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, McInnes M, Phillips ML, Trivedi MH, Weissman MM, & Shinohara RT (2017) <doi:10.1016/j.neuroimage.2017.11.024> "Harmonization of cortical thickness measurements across scanners and sites." Fortin J-P, Parker D, Tun<e7> B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE, Schultz RT, Verma R, & Shinohara RT (2017) <doi:10.1016/j.neuroimage.2017.08.047> "Harmonization of multi-site diffusion tensor imaging data.".

Second and backward-incompatible version of R package eodhd <https://eodhd.com/>, extended with a cache and quota system, also offering functions for cleaning and aggregating the financial data.

This package provides tools for working with iEEG matrix data, including downloading curated iEEG data from OSF (The Open Science Framework <https://osf.io/>) (EpochDownloader()), making new objects (Epoch()), processing (crop() and resample()), and visualizing the data (plot()).

Analysis and visualization of similarities between epilepsy ontologies based on text mining results by comparing ranked lists of co-occurring drug terms in the BioASQ corpus. The ranked result lists of neurological drug terms co-occurring with terms from the epilepsy ontologies EpSO, ESSO, EPILONT, EPISEM and FENICS undergo further analysis. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>, and Mueller, Bernd et al. (2022) <doi:10.1186/s13326-021-00258-w>.

Goodness-of-fit tests for selection of r in the r-largest order statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the Generalized Pareto distribution (GPD). Random number generation and density functions for the GEVr distribution. Profile likelihood for return level estimation using the GEVr and Generalized Pareto distributions. P-value adjustments for sequential, multiple testing error control. Non-stationary fitting of GEVr and GPD. Bader, B., Yan, J. & Zhang, X. (2016) <doi:10.1007/s11222-016-9697-3>. Bader, B., Yan, J. & Zhang, X. (2018) <doi:10.1214/17-AOAS1092>.

Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases (see Fay (2010) <doi:10.32614/rj-2010-008>).

Variable selection methods have been extensively developed for analyzing highdimensional omics data within both the frequentist and Bayesian frameworks. This package provides implementations of the spike-and-slab quantile (group) LASSO which have been developed along the line of Bayesian hierarchical models but deeply rooted in frequentist regularization methods by utilizing Expectationâ Maximization (EM) algorithm. The spike-and-slab quantile LASSO can handle data irregularity in terms of skewness and outliers in response variables, compared to its non-robust alternative, the spike-and-slab LASSO, which has also been implemented in the package. In addition, procedures for fitting the spike-and-slab quantile group LASSO and its non-robust counterpart have been implemented in the form of quantile/least-square varying coefficient mixed effect models for high-dimensional longitudinal data. The core module of this package is developed in C++'.

This package implements the Enhanced Portfolio Optimization (EPO) method as described in Pedersen, Babu and Levine (2021) <doi:10.2139/ssrn.3530390>.

This cointegration based Time Delay Neural Network Model hybrid model allows the researcher to make use of the information extracted by the cointegrating vector as an input in the neural network model.

An intuitive and user-friendly package designed to aid undergraduate students in understanding and applying econometric methods in their studies, Tailored specifically for Econometrics and Regression Modeling courses, it provides a practical toolkit for modeling and analyzing econometric data with detailed inference capabilities.

This package provides a robust and efficient solution for working with Ethiopian dates. It can seamlessly convert to and from Gregorian dates. It is designed to be compatible with the tidyverse data workflow, including plotting with ggplot2'. It ensures lightning-fast computations by integrating high-performance C++ code through Rcpp package.

Calculates the empirical likelihood ratio and p-value for a mean-type hypothesis (or multiple mean-type hypotheses) based on two samples with possible censored data.

This package provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). Our JSS article, Willwerscheid, Carbonetto, and Stephens (2025) <doi:10.18637/jss.v114.i03>, provides a detailed introduction to the package.

This package provides several confidence interval and testing procedures using event-specific win ratios for semi-competing risks data with non-terminal and terminal events, as developed in Yang et al. (2021<doi:10.1002/sim.9266>). Compared with conventional methods for survival data, these procedures are designed to utilize more data for improved inference procedures with semi-competing risks data. The event-specific win ratios were introduced in Yang and Troendle (2021<doi:10.1177/1740774520972408>). In this package, the event-specific win ratios and confidence intervals are obtained for each event type, and several testing procedures are developed for the global null of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazard assumptions, under which the event-specific win ratios converge to the hazard ratios, and a test of equal hazard ratios are provided. For summarizing the treatment effect on all events, confidence intervals for linear combinations of the event-specific win ratios are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al (2021<doi:10.1002/sim.9266>). Also, transformations are used to yield better control of the type one error rates for moderately sized data sets.

Computes and plots a transformed empirical CDF (ecdf) as a diagnostic for heavy tailed data, specifically data with power law decay on the tails. Routines for annotating the plot, comparing data to a model, fitting a nonparametric model, and some multivariate extensions are given.