This package provides functions for analysis of rate changes in sequential events.

Exploratory and descriptive analysis of event based data. Provides methods for describing and selecting process data, and for preparing event log data for process mining. Builds on the S3-class for event logs implemented in the package bupaR'.

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 implements the Ebrahim-Farrington goodness-of-fit test for logistic regression models, particularly effective for sparse data and binary outcomes. This test provides an improved alternative to the traditional Hosmer-Lemeshow test by using a modified Pearson chi-square statistic with data-dependent grouping. The test is based on Farrington (1996) theoretical framework but simplified for practical implementation with binary data. Includes functions for both the original Farrington test (for grouped data) and the new Ebrahim-Farrington test (for binary data with automatic grouping). For more details see Hosmer (1980) <doi:10.1080/03610928008827941> and Farrington (1996) <doi:10.1111/j.2517-6161.1996.tb02086.x>.

This package provides tools to download and manipulate the Permanent Household Survey from Argentina (EPH is the Spanish acronym for Permanent Household Survey). e.g: get_microdata() for downloading the datasets, get_poverty_lines() for downloading the official poverty baskets, calculate_poverty() for the calculation of stating if a household is in poverty or not, following the official methodology. organize_panels() is used to concatenate observations from different periods, and organize_labels() adds the official labels to the data. The implemented methods are based on INDEC (2016) <http://www.estadistica.ec.gba.gov.ar/dpe/images/SOCIEDAD/EPH_metodologia_22_pobreza.pdf>. As this package works with the argentinian Permanent Household Survey and its main audience is from this country, the documentation was written in Spanish.

Runs an evolutionary algorithm using the AlphaSimR machinery <doi:10.1093/g3journal/jkaa017> .

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.

This package provides a comprehensive collection of utility functions for data analysis and visualization in R. The package provides 60+ functions for data manipulation, file handling, color palette management, bioinformatics workflows, statistical analysis, 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.

Estimates power by simulation for multivariate abundance data to be used for sample size estimates. Multivariate equivalence testing by simulation from a Gaussian copula model. The package also provides functions for parameterising multivariate effect sizes and simulating multivariate abundance data jointly. The discrete Gaussian copula approach is described in Popovic et al. (2018) <doi:10.1016/j.jmva.2017.12.002>.

Fast and easy computation of Euclidean Minimum Spanning Trees (EMST) from data, relying on the R API for mlpack - the C++ Machine Learning Library (Curtin et. al., 2013). emstreeR uses the Dual-Tree Boruvka (March, Ram, Gray, 2010, <doi:10.1145/1835804.1835882>), which is theoretically and empirically the fastest algorithm for computing an EMST. This package also provides functions and an S3 method for readily visualizing Minimum Spanning Trees (MST) using either the style of the base', scatterplot3d', or ggplot2 libraries; and functions to export the MST output to shapefiles.

Method and tool for generating time series forecasts using an ensemble wavelet-based auto-regressive neural network architecture. This method provides additional support of exogenous variables and also generates confidence interval. This package provides EWNet model for time series forecasting based on the algorithm by Panja, et al. (2022) and Panja, et al. (2023) <arXiv:2206.10696> <doi:10.1016/j.chaos.2023.113124>.

The equality of a large number k of densities is tested by measuring the L2 distance between the corresponding kernel density estimators and the one based on the pooled sample. The test even works for sample sizes as small as 2.

Interface to Eurostatâ s API (SDMX 2.1) with fast data.table-based import of data, labels, and metadata. On top of the core functionality, data search and data description/comparison functions are also provided. Use <https://github.com/alekrutkowski/eurodata_codegen> â a point-and-click app for rapid and easy generation of richly-commented R code â to import a Eurostat dataset or its subset (based on the eurodata::importData() function).

This package performs analysis of polynomial regression in simple designs with quantitative treatments.

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>.

This package provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) <doi:10.1207/s15327906mbr0102_10>, Kaiser-Guttman Criterion (KGC) by Guttman (1954) <doi:10.1007/BF02289162> and Kaiser (1960) <doi:10.1177/001316446002000116>, and flexible Parallel Analysis (PA) by Horn (1965) <doi:10.1007/BF02289447> based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) <doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and Roche (2012) <doi:10.1037/a0025697>, and Hull method by Lorenzo-Seva et al. (2011) <doi:10.1080/00273171.2011.564527>, as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) <doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by Goretzko and Buhner (2020) <doi:10.1037/met0000262>. Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors.

This package provides a tool to operate a batch of univariate or multivariate Cox models and return tidy result.

This package provides an interface to the European Central Bank's Data Portal API, allowing for programmatic retrieval of a vast quantity of statistical data.

Set of functions to keep track and find objects in user-defined environments by identifying environments by name --which cannot be retrieved with the built-in function environmentName(). The package also provides functionality to obtain simplified information about function calling chains and to get an object's memory address.

This package provides a collection of functions for microbial ecology and other applications of genomics and metagenomics. Companion package for the Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016 <DOI:10.7287/peerj.preprints.1900v1>).

Predicts enrollment and events assumed enrollment and treatment-specific time-to-event models, and calculates test statistics for time-to-event data with cured population based on the simulation.Methods for prediction event in the existence of cured population are as described in : Chen, Tai-Tsang(2016) <doi:10.1186/s12874-016-0117-3>.

Interconverts between ordered lists and compact string notation. Useful for capturing code lists, and pair-wise codes and decodes, for text storage. Analogous to factor levels and labels. Generics encode() and decode() perform interconversion, while codes() and decodes() extract components of an encoding. The function encoded() checks whether something is interpretable as an encoding. If a vector has an encoded guide attribute, as_factor() uses it to coerce to factor.

This package provides a framework to build and evaluate diagnosis or prognosis models using stacking, voting, and bagging ensemble techniques with various base learners. The package also includes tools for visualization and interpretation of models. The development version of the package is available on GitHub at <https://github.com/xiaojie0519/E2E>. The methods are based on the foundational work of Breiman (1996) <doi:10.1007/BF00058655> on bagging and Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> on stacking.

Streamlines the fitting of common Bayesian item response models using Stan.