Import SPSS data, handle and change SPSS meta data, store and access large hierarchical data in SQLite data bases.

Diagnose, visualize, and aggregate event report level data to the event level. Users provide an event report level dataset, specify their aggregation rules, and the package produces a dataset aggregated at the event level. Also includes the Modes and Agents of Election-Related Violence in Côte d'Ivoire and Kenya (MAVERICK) dataset, an event report level dataset that records all documented instances of electoral violence from the first multiparty election to 2022 in Côte d'Ivoire (1995-2022) and Kenya (1992-2022).

Elastic net regression models are controlled by two parameters, lambda, a measure of shrinkage, and alpha, a metric defining the model's location on the spectrum between ridge and lasso regression. glmnet provides tools for selecting lambda via cross validation but no automated methods for selection of alpha. Elastic Net SearcheR automates the simultaneous selection of both lambda and alpha. Developed, in part, with support by NICHD R03 HD094912.

This package performs analyzes and estimates of environmental covariates and genetic parameters related to selection strategies and development of superior genotypes. It has two main functionalities, the first being about prediction models of covariates and environmental processes, while the second deals with the estimation of genetic parameters and selection strategies. Designed for researchers and professionals in genetics and environmental sciences, the package combines statistical methods for modeling and data analysis. This includes the plastochron estimate proposed by Porta et al. (2024) <doi:10.1590/1807-1929/agriambi.v28n10e278299>, Stress indices for genotype selection referenced by Ghazvini et al. (2024) <doi:10.1007/s10343-024-00981-1>, the Environmental Stress Index described by Tazzo et al. (2024) <https://revistas.ufg.br/vet/article/view/77035>, industrial quality indices of wheat genotypes (Szareski et al., 2019), <doi:10.4238/gmr18223>, Ear Indexes estimation (Rigotti et al., 2024), <doi:10.13083/reveng.v32i1.17394>, Selection index for protein and grain yield (de Pelegrin et al., 2017), <doi:10.4236/ajps.2017.813224>, Estimation of the ISGR - Genetic Selection Index for Resilience for environmental resilience (Bandeira et al., 2024) <https://www.cropj.com/Carvalho_18_12_2024_825_830.pdf>, estimation of Leaf Area Index (Meira et al., 2015) <https://www.fag.edu.br/upload/revista/cultivando_o_saber/55d1ef202e494.pdf>, Restriction of control variability (Carvalho et al., 2023) <doi:10.4025/actasciagron.v45i1.56156>, Risk of Disease Occurrence in Soybeans described by Engers et al. (2024) <doi:10.1007/s40858-024-00649-1> and estimation of genetic parameters for selection based on balanced experiments (Yadav et al., 2024) <doi:10.1155/2024/9946332>.

Estimates linear panel event study models. Plots coefficients following the recommendations in Freyaldenhoven et al. (2021) <doi:10.3386/w29170>. Includes sup-t bands, testing for key hypotheses, least wiggly path through the Wald region. Allows instrumental variables estimation following Freyaldenhoven et al. (2019) <doi:10.1257/aer.20180609>.

Statistics and graphics for streamflow history, water quality trends, and the statistical modeling algorithm: Weighted Regressions on Time, Discharge, and Season (WRTDS).

This package provides a shiny gadget to create ggplot2 figures interactively with drag-and-drop to map your variables to different aesthetics. You can quickly visualize your data accordingly to their type, export in various formats, and retrieve the code to reproduce the plot.

Compute common data quality metrics for accuracy, precision and data loss for screen-based eye trackers. Supports input data both in pixels on the screen and in degrees, output measures are (where appropriate) expressed as angles in degrees.

This package provides measures to characterize the complexity of classification and regression problems based on aspects that quantify the linearity of the data, the presence of informative feature, the sparsity and dimensionality of the datasets. This package provides bug fixes, generalizations and implementations of many state of the art measures. The measures are described in the papers: Lorena et al. (2019) <doi:10.1145/3347711> and Lorena et al. (2018) <doi:10.1007/s10994-017-5681-1>.

Various recursive two-stage models to address the endogeneity issue of treatment variables in observational study or mediators in experiments. The details of the models are discussed in Peng (2023) <doi:10.1287/isre.2022.1113>.

This package provides a comprehensive collection of datasets related to education, covering topics such as student performance, learning methods, test scores, absenteeism, and other educational metrics. This package serves as a resource for educational researchers, data analysts, and statisticians to explore and analyze data in the field of education.

Functions, data sets and shiny apps for "Epidemics: Models and Data in R" by Ottar N. Bjornstad (ISBN 978-3-319-97487-3) <https://www.springer.com/gp/book/9783319974866>. The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course <https://www.coursera.org/learn/epidemics>.

Perform dynamic model averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.

Extreme value theory, nonparametric kernel estimation, tail conditional probabilities, extreme conditional quantile, adaptive estimation, quantile regression, survival probabilities.

Take the examples written in your documentation of functions and use them to create shells (skeletons which must be manually completed by the user) of test files to be tested with the testthat package. Sort of like python doctests for R.

Analyses districted electoral systems of any magnitude by computing district-party conversion ratios and seats-to-votes deviations, decomposing the sources of deviation. Traditional indexes are also computed. References: Kedar, O., Harsgor, L. and Sheinerman, R.A. (2016). <doi:10.1111/ajps.12225>. Penades, A and Pavia, J.M. (2025) The decomposition of seats-to-votes distortion in elections: mean, variance, malapportionment and participation''. Acknowledgements: The authors wish to thank Consellerà a de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIACO/2023/031) for supporting this research.

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.

Expectile and quantile regression of models with nonlinear effects e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes as well as boosting; also supplies expectiles for common distributions.

This package provides a tool to draw samples from a Empirical Likelihood Bayesian posterior of parameters using Hamiltonian Monte Carlo.

This package provides functions of five estimation method for ED50 (50 percent effective dose) are provided, and they are respectively Dixon-Mood method (1948) <doi:10.2307/2280071>, Choi's original turning point method (1990) <doi:10.2307/2531453> and it's modified version given by us, as well as logistic regression and isotonic regression. Besides, the package also supports comparison between two estimation results.

This package provides methods to deal with the free antiassociative algebra over the reals with an arbitrary number of indeterminates. Antiassociativity means that (xy)z = -x(yz). Antiassociative algebras are nilpotent with nilindex four (Remm, 2022, <doi:10.48550/arXiv.2202.10812>) and this drives the design and philosophy of the package. Methods are defined to create and manipulate arbitrary elements of the antiassociative algebra, and to extract and replace coefficients. A vignette is provided.

This package provides EIOPA (European Insurance And Occupational Pensions Authority) risk-free rates. Please note that the author of this package is not affiliated with EIOPA. The data is accessed through a REST API available at <https://mehdiechchelh.com/api/>.

Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) <doi:10.1002/hyp.10953>.

The confusion matrix (CM) is used to get a classifier's evaluation measure in order to select a method among many. A stochastic matrix and its transformation are computed from the CM. The eigenvalues of the transformed symmetric matrix are used to get an entropy which appears to be a good evaluation measure. Many other measures, commonly used, are provided for comparison purpose.