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.

Production efficiency and economic efficiency are crucial concepts in agriculture/horticulture for sustainable and profitable practices. It helps to determine the optimal use of resources to maximize outputs and profitability. Production efficiency focuses on the optimal use of resources to produce goods, while economic efficiency ensures these goods are produced and allocated in a way that maximizes economic welfare. Production efficiency and economic efficiency are calculated with the help of the formula given by (Kumar et al., 2017) <doi:10.21921/jas.v4i04.10202>.

Inspect survival data, plot Kaplan-Meier curves, assess the proportional hazards assumption, fit parametric survival models, predict and plot survival and hazards, and export the outputs to Excel'. A simple interface for fitting survival models using flexsurv::flexsurvreg(), flexsurv::flexsurvspline(), flexsurvcure::flexsurvcure(), and survival::survreg().

The main aim is to further facilitate the creation of exercises based on the package exams by Grün, B., and Zeileis, A. (2009) <doi:10.18637/jss.v029.i10>. Creating effective student exercises involves challenges such as creating appropriate data sets and ensuring access to intermediate values for accurate explanation of solutions. The functionality includes the generation of univariate and bivariate data including simple time series, functions for theoretical distributions and their approximation, statistical and mathematical calculations for tasks in basic statistics courses as well as general tasks such as string manipulation, LaTeX/HTML formatting and the editing of XML task files for Moodle'.

An information-theoretic diagnostic technique for estimating the intrinsic dimensionality of tabular datasets. Evaluates shared probability mass via a transformed mutual information metric. Aims to extract the Intrinsic Generative Rank (r) and structural topology. For full methodological details, see the preprint by Lee-St. John (2026) <https://zenodo.org/records/22028087>.

Calculate cutoff values for model fit measures used in structural equation modeling (SEM) by simulating and testing data sets (cf. Hu & Bentler, 1999 <doi:10.1080/10705519909540118>) with the same parameters (population model, number of observations, etc.) as the model under consideration.

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>.

Implementation of a modular framework for ecosystem risk assessments, combining existing risk assessment approaches tailored to semi-quantitative and quantitative analyses.

This package provides a SQLite database is designed to store all information of experiment-based data including metadata, experiment design, managements, phenotypic values and climate records. The dataset can be imported from an Excel file.

Three sets of data and functions for informing ecosystem restoration decisions, particularly in the context of the U.S. Army Corps of Engineers. First, model parameters are compiled as a data set and associated metadata for over 300 habitat suitability models developed by the U.S. Fish and Wildlife Service (USFWS 1980, <https://www.fws.gov/policy-library/870fw1>). Second, functions for conducting habitat suitability analyses both for the models described above as well as generic user-specified model parameterizations. Third, a suite of decision support tools for conducting cost-effectiveness and incremental cost analyses (Robinson et al. 1995, IWR Report 95-R-1, U.S. Army Corps of Engineers).

Structure mining from XGBoost and LightGBM models. Key functionalities of this package cover: visualisation of tree-based ensembles models, identification of interactions, measuring of variable importance, measuring of interaction importance, explanation of single prediction with break down plots (based on xgboostExplainer and iBreakDown packages). To download the LightGBM use the following link: <https://github.com/Microsoft/LightGBM>. EIX is a part of the DrWhy.AI universe.

Evaluates the performance of binary classifiers. Computes confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR, accuracy, F1, DOR, ..), and area under the curve. Outputs are well suited for nested dataframes.

This package provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.

Generates interactive circle plots with the nodes around the circumference and linkages between the connected nodes using hierarchical edge bundling via the D3 JavaScript library. See <http://d3js.org/> for more information on D3.

This package provides a set of extensions for the ergm package to fit weighted networks whose edge weights are counts. See Krivitsky (2012) <doi:10.1214/12-EJS696> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.

This package provides tools for calculating evolvability parameters from estimated G-matrices as defined in Hansen and Houle (2008) <doi:10.1111/j.1420-9101.2008.01573.x> and fits phylogenetic comparative models that link the rate of evolution of a trait to the state of another evolving trait (see Hansen et al. 2021 Systematic Biology <doi:10.1093/sysbio/syab079>). The package was released with Bolstad et al. (2014) <doi:10.1098/rstb.2013.0255>, which contains some examples of use.

This package provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).

Augments the eiCompare package's Racially Polarized Voting (RPV) functionality to streamline analyses and visualizations used to support voting rights and redistricting litigation. The package implements methods described in Barreto, M., Collingwood, L., Garcia-Rios, S., & Oskooii, K. A. (2022). "Estimating Candidate Support in Voting Rights Act Cases: Comparing Iterative EI and EI-RÃ C Methods" <doi:10.1177/0049124119852394>.

Analyzes and quantifies ecosystem multifunctionality with functions to calculate multifunctionality richness (MFric), multifunctionality divergence (MFdiv), and multifunctionality regularity (MFreg). These indices help assess the relationship between biodiversity and multiple ecosystem functions. For more details, see Byrnes et al. (2014) <doi:10.1111/2041-210X.12143> and Chao et al. (2024) <doi:10.1111/ele.14336>.

Compute common data quality metrics for accuracy, precision and data loss for screen-based eye trackers. The package supports gaze input in screen pixels or degrees and reports angular measures in degrees where appropriate. If you use this package, please cite Niehorster, D.C., Nyström, M., Hessels, R.S., Benjamins, J.S., Andersson, R., and Hooge, I.T.C. (2026). The fundamentals of eye tracking, Part 7: Determining data quality. Behavior Research Methods. <doi:10.3758/s13428-026-03039-4>.

Connect to Elasticsearch and OpenSearch', NoSQL databases built on the Java Virtual Machine and using the Apache Lucene library. Interacts with the Elasticsearch HTTP API (<https://www.elastic.co/elasticsearch/>) and the OpenSearch HTTP API (<https://opensearch.org/>). Includes functions for setting connection details to Elasticsearch and OpenSearch instances, loading bulk data, searching for documents with both HTTP query variables and JSON based body requests. In addition, elastic provides functions for interacting with APIs for indices', documents, nodes, clusters, an interface to the cat API, and more.

The Economic Policy Institute (<https://www.epi.org/>) provides researchers, media, and the public with easily accessible, up-to-date, and comprehensive historical data on the American labor force. It is compiled from Economic Policy Institute analysis of government data sources. Use it to research wages, inequality, and other economic indicators over time and among demographic groups. Data is usually updated monthly.

Application of empirical mode decomposition based artificial neural network model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=55&issue=1&article=013>; (ii) Das (2020) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=56&issue=2&article=002>.

Treats language model evaluations as statistical experiments and supplies the inference they require. Provides central limit theorem and cluster-robust standard errors for evaluation scores, paired and unpaired model comparisons, variance decomposition when several responses are drawn per question, control-variate variance reduction, multiplicity adjustment across benchmark suites, and power and minimum detectable effect calculations for planning evaluations, following Miller (2024) <doi:10.48550/arXiv.2411.00640>. For evaluations scored by a model judge, implements agreement statistics against a human gold standard and prediction-powered inference (Angelopoulos et al. 2023) <doi:10.1126/science.adi6000> with the power-tuned estimator of Angelopoulos, Bates and Jordan (2023) <doi:10.48550/arXiv.2311.01453>, so a small set of human labels debiases a large set of judge scores. Leaderboards are supported through bootstrap rank intervals and Bradley-Terry ratings (Bradley and Terry 1952) <doi:10.2307/2334029>. Accepts scores from any evaluation harness.