Initially designed to distribute code for estimating the Gaussian graphical model with Lasso regularization, also known as the graphical lasso (glasso), using an Expectation-Maximization (EM) algorithm based on work by Städler and Bühlmann (2012) <doi:10.1007/s11222-010-9219-7>. As a byproduct, code for estimating means and covariances (or the precision matrix) under a multivariate normal (Gaussian) distribution is also available.

This package provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools. Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) <doi:10.12688/f1000research.7082.3>.

This package provides empirical likelihood-based methods for the inference of variance components in linear mixed-effects models.

This package provides a comprehensive toolkit for discovering differential and difference equations from empirical time series data using symbolic regression. The package implements a complete workflow from data preprocessing (including Total Variation Regularized differentiation for noisy economic data), visual exploration of dynamical structure, and symbolic equation discovery via genetic algorithms. It leverages a high-performance Julia backend ('SymbolicRegression.jl') to provide industrial-grade robustness, physics-informed constraints, and rigorous out-of-sample validation. Designed for economists, physicists, and researchers studying dynamical systems from observational data.

Package implements entropy balancing, a data preprocessing procedure described in Hainmueller (2008, <doi:10.1093/pan/mpr025>) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.

The experiment selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE) aims to select the experiment that optimizes the bias-variance tradeoff for estimating a causal average treatment effect (ATE) where different experiments may include a randomized controlled trial (RCT) alone or an RCT combined with real-world data. Using cross-validation, the ES-CVTMLE separates the selection of the optimal experiment from the estimation of the ATE for the chosen experiment. The estimated bias term in the selector is a function of the difference in conditional mean outcome under control for the RCT compared to the combined experiment. In order to help include truly unbiased external data in the analysis, the estimated average treatment effect on a negative control outcome may be added to the bias term in the selector. For more details about this method, please see Dang et al. (2022) <arXiv:2210.05802>.

This package provides a system to facilitate designing comparative (and non-comparative) experiments using the grammar of experimental designs <https://emitanaka.org/edibble-book/>. An experimental design is treated as an intermediate, mutable object that is built progressively by fundamental experimental components like units, treatments, and their relation. The system aids in experimental planning, management and workflow.

Import gaze data from edf files generated by the SR Research <https://www.sr-research.com/> EyeLink eye tracker. Gaze data, both recorded events and samples, is imported per trial. The package allows to extract events of interest, such as saccades, blinks, etc. as well as recorded variables and custom events (areas of interest, triggers) into separate tables. The package requires EDF API library that can be obtained at <https://www.sr-research.com/support/>.

This package contains match results from seven European men's football leagues, namely Premier League (England), Ligue 1 (France), Bundesliga (Germany), Serie A (Italy), Primera Division (Spain), Eredivisie (The Netherlands), Super Lig (Turkey). Includes Seasons 2010/2011 until 2019/2020 and a set of interesting covariates. Can be used all purposes.

DNA methylation (6mA) is a major epigenetic process by which alteration in gene expression took place without changing the DNA sequence. Predicting these sites in-vitro is laborious, time consuming as well as costly. This EpiSemble package is an in-silico pipeline for predicting DNA sequences containing the 6mA sites. It uses an ensemble-based machine learning approach by combining Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting approach to predict the sequences with 6mA sites in it. This package has been developed by using the concept of Chen et al. (2019) <doi:10.1093/bioinformatics/btz015>.

In the USA, companies file different forms with the U.S. Securities and Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis, and Retrieval system). The EDGAR database automated system collects all the different necessary filings and makes it publicly available. This package facilitates retrieving, storing, searching, and parsing of all the available filings on the EDGAR server. It downloads filings from SEC server in bulk with a single query. Additionally, it provides various useful functions: extracts 8-K triggering events, extract "Business (Item 1)" and "Management's Discussion and Analysis(Item 7)" sections of annual statements, searches filings for desired keywords, provides sentiment measures, parses filing header information, and provides HTML view of SEC filings.

Automatic generation of quizzes or individual questions as (interactive) forms within rmarkdown or quarto documents based on R/exams exercises.

EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.

This package provides a set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations. The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2022) <doi:10.3150/21-BEJ1375>, Davison et al. (2023) <doi:10.1080/07350015.2022.2078332>, Daouia et al. (2018) <doi:10.1111/rssb.12254>, Drees (2000) <doi:10.1214/aoap/1019487617>, Drees (2003) <doi:10.3150/bj/1066223272>, de Haan and Ferreira (2006) <doi:10.1007/0-387-34471-3>, de Haan et al. (2016) <doi:10.1007/s00780-015-0287-6>, Padoan and Rizzelli (2024) <doi:10.3150/23-BEJ1668>, Daouia et al. (2024) <doi:10.3150/23-BEJ1632>.

Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data. Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.

Este paquete pretende apoyar el proceso enseñanza-aprendizaje de estadà stica descriptiva e inferencial. Las funciones contenidas en el paquete estadistica cubren los conceptos básicos estudiados en un curso introductorio. Muchos conceptos son ilustrados con gráficos dinámicos o web apps para facilitar su comprensión. This package aims to help the teaching-learning process of descriptive and inferential statistics. The functions contained in the package estadistica cover the basic concepts studied in a statistics introductory course. Many concepts are illustrated with dynamic graphs or web apps to make the understanding easier. See: Esteban et al. (2005, ISBN: 9788497323741), Newbold et al.(2019, ISBN:9781292315034 ), Murgui et al. (2002, ISBN:9788484424673) .

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>.

Extract features from tabular data in a declarative fashion, with a focus on processing medical records. Features are specified as JSON and are independently processed before being joined. Input data can be provided as CSV files or as data frames. This setup ensures that data is transformed in a modular and reproducible manner, and allows the same pipeline to be easily applied to new data.

Automation of the item selection processes for Rasch scales by means of exhaustive search for suitable Rasch models (dichotomous, partial credit, rating-scale) in a list of item-combinations. The item-combinations to test can be either all possible combinations or item-combinations can be defined by several rules (forced inclusion of specific items, exclusion of combinations, minimum/maximum items of a subset of items). Tests for model fit and item fit include ordering of the thresholds, item fit-indices, likelihood ratio test, Martin-Löf test, Wald-like test, person-item distribution, person separation index, principal components of Rasch residuals, empirical representation of all raw scores or Rasch trees for detecting differential item functioning. The tests, their ordering and their parameters can be defined by the user. For parameter estimation and model tests, functions of the packages eRm', psychotools or pairwise can be used.

Create forecasts from multiple predictions using ensemble Bayesian model averaging (EBMA). EBMA models can be estimated using an expectation maximization (EM) algorithm or as fully Bayesian models via Gibbs sampling. The methods in this package are Montgomery, Hollenbach, and Ward (2015) <doi:10.1016/j.ijforecast.2014.08.001> and Montgomery, Hollenbach, and Ward (2012) <doi:10.1093/pan/mps002>.

Goodness-of-fit tests for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. Monte Carlo methods are provided to make the package capable of solving high-dimensional problems.

An RStudio addin for editing a data.frame or a tibble'. You can delete, add or update a data.frame without coding. You can get resultant data as a data.frame'. In the package, modularized shiny app codes are provided. These modules are intended for reuse across applications.

This SVG elements generator can easily generate SVG elements such as rect, line, circle, ellipse, polygon, polyline, text and group. Also, it can combine and output SVG elements into a SVG file.

Conduct one- and two-sample goodness-of-fit tests for univariate data. In the one-sample case, normal, uniform, exponential, Bernoulli, binomial, geometric, beta, Poisson, lognormal, Laplace, asymmetric Laplace, inverse Gaussian, half-normal, chi-squared, gamma, F, Weibull, Cauchy, and Pareto distributions are supported. egof.test() can also test goodness-of-fit to any distribution with a continuous distribution function. A subset of the available distributions can be tested for the composite goodness-of-fit hypothesis, that is, one can test for distribution fit with unknown parameters. P-values are calculated via parametric bootstrap.