This package contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests.

The purpose of this package is to estimate the potential of urban agriculture to contribute to addressing several urban challenges at the city-scale. Within this aim, we selected 8 indicators directly related to one or several urban challenges. Also, a function is provided to compute new scenarios of urban agriculture. Methods are described by Pueyo-Ros, Comas & Corominas (2023) <doi:10.12688/openreseurope.16054.1>.

This package provides tools for modelling electric vehicle charging sessions into generic groups with similar connection patterns called "user profiles", using Gaussian Mixture Models clustering. The clustering and profiling methodology is described in Cañigueral and Meléndez (2021, ISBN:0142-0615) <doi:10.1016/j.ijepes.2021.107195>.

Predictors can be converted to one or more numeric representations using a variety of methods. Effect encodings using simple generalized linear models <doi:10.48550/arXiv.1611.09477> or nonlinear models <doi:10.48550/arXiv.1604.06737> can be used. There are also functions for dimension reduction and other approaches.

Addresses tasks along the pipeline from raw data to analysis and visualization for eye-tracking data. Offers several popular types of analyses, including linear and growth curve time analyses, onset-contingent reaction time analyses, as well as several non-parametric bootstrapping approaches. For references to the approach see Mirman, Dixon & Magnuson (2008) <doi:10.1016/j.jml.2007.11.006>, and Barr (2008) <doi:10.1016/j.jml.2007.09.002>.

Facilitates the aggregation of species geographic ranges from vector or raster spatial data, and that enables the calculation of various morphological and phylogenetic community metrics across geography. Citation: Title, PO, DL Swiderski and ML Zelditch (2022) <doi:10.1111/2041-210X.13914>.

This package provides an interface to the public APIs of the European and Mediterranean Plant Protection Organization (EPPO) database. It enables users to retrieve EPPO data by accessing specific services and datasets. The package also includes utilities for data wrangling, including the integration of taxonomy with rank information. See <https://data.eppo.int/apis/> for more details.

Power analysis is used in the estimation of sample sizes for experimental designs. Most programs and R packages will only output the highest recommended sample size to the user. Often the user input can be complicated and computing multiple power analyses for different treatment comparisons can be time consuming. This package simplifies the user input and allows the user to view all of the sample size recommendations or just the ones they want to see. The calculations used to calculate the recommended sample sizes are from the pwr package.

Estimation of the parameters in a model for symmetric relational data (e.g., the above-diagonal part of a square matrix), using a model-based eigenvalue decomposition and regression. Missing data is accommodated, and a posterior mean for missing data is calculated under the assumption that the data are missing at random. The marginal distribution of the relational data can be arbitrary, and is fit with an ordered probit specification. See Hoff (2007) <doi:10.48550/arXiv.0711.1146>. for details on the model.

Simulation of Electric Vehicles charging sessions using Gaussian models, together with time-series power demand calculations.

This package provides functions to quantify animal dominance hierarchies. The major focus is on Elo rating and its ability to deal with temporal dynamics in dominance interaction sequences. For static data, David's score and de Vries I&SI are also implemented. In addition, the package provides functions to assess transitivity, linearity and stability of dominance networks. See Neumann et al (2011) <doi:10.1016/j.anbehav.2011.07.016> for an introduction.

This package provides a toolset for generating Ecological Limit Function (ELF) models and evaluating potential species loss resulting from flow change, based on the elfgen framework. ELFs describe the relation between aquatic species richness (fish or benthic macroinvertebrates) and stream size characteristics (streamflow or drainage area). Journal publications are available outlining framework methodology (Kleiner et al. (2020) <doi:10.1111/1752-1688.12876>) and application (Rapp et al. (2020) <doi:10.1111/1752-1688.12877>).

Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. EL calculations are based on J. Valeinis, E. Cers (2011) <http://home.lu.lv/~valeinis/lv/petnieciba/EL_TwoSample_2011.pdf>.

Presents a statistical method that uses a recursive algorithm for signal extraction. The method handles a non-parametric estimation for the correlation of the errors. See "Krivobokova", "Serra", "Rosales" and "Klockmann" (2021) <arXiv:1812.06948> for details.

Package for data exploration and result presentation. Full epicalc package with data management functions is available at <https://medipe.psu.ac.th/epicalc/>'.

Several functions, datasets, and sample codes related to empirical research in economics are included. They cover the marginal effects for binary or ordered choice models, static and dynamic Almost Ideal Demand System (AIDS) models, and a typical event analysis in finance.

This package provides functions for the echelon analysis proposed by Myers et al. (1997) <doi:10.1023/A:1018518327329>, and the detection of spatial clusters using echelon scan method proposed by Kurihara (2003) <doi:10.20551/jscswabun.15.2_171>.

The purpose of this package is to generate trees and validate unverified code. Trees are made by parsing a statement into a verification tree data structure. This will make it easy to port the statement into another language. Safe statement evaluations are done by executing the verification trees.

Enables users to incorporate expert opinion with parametric survival analysis using a Bayesian or frequentist approach. Expert Opinion can be provided on the survival probabilities at certain time-point(s) or for the difference in mean survival between two treatment arms. Please reference it's use as Cooney, P., White, A. (2023) <doi:10.1177/0272989X221150212>.

This package implements likelihood-based evidence ratios for unified reporting in classical statistical testing. The package reports effect estimates, uncertainty intervals, and likelihood ratios on the log 10 scale derived from a single statistical model. It applies to standard normal mean tests, contingency tables, and regression coefficients, and provides a direct evidential measure while retaining classical error guarantees. For the Evidence Ratio Reporting Standard see Lawless (2026) <doi:10.5281/zenodo.18261076>.

Analysis of trade in value added with international input-output tables. Includes commands for easy data extraction, matrix manipulation, decomposition of value added in gross exports and calculation of value added indicators, with full geographical and sector customization. Decomposition methods include Borin and Mancini (2023) <doi:10.1080/09535314.2022.2153221>, Miroudot and Ye (2021) <doi:10.1080/09535314.2020.1730308>, Wang et al. (2013) <https://econpapers.repec.org/paper/nbrnberwo/19677.htm> and Koopman et al. (2014) <doi:10.1257/aer.104.2.459>.

Provide the EMU Speech Database Management System (EMU-SDMS) with database management, data extraction, data preparation and data visualization facilities. See <https://ips-lmu.github.io/The-EMU-SDMS-Manual/> for more details.

This package provides functions are provided to determine production frontiers and technical efficiency measures through non-parametric techniques based upon regression trees. The package includes code for estimating radial input, output, directional and additive measures, plotting graphical representations of the scores and the production frontiers by means of trees, and determining rankings of importance of input variables in the analysis. Additionally, an adaptation of Random Forest by a set of individual Efficiency Analysis Trees for estimating technical efficiency is also included. More details in: <doi:10.1016/j.eswa.2020.113783>.

Data published by the United States Federal Energy Regulatory Commission including electric company financial data, natural gas company financial data, hydropower plant data, liquified natural gas plant data, oil company financial data natural gas company financial data, and natural gas storage field data.