This package provides a shiny'-based graphical user interface for the earth package, enabling interactive building and exploration of Multivariate Adaptive Regression Splines (MARS) models. Features include data import from CSV and Excel files, automatic detection of categorical variables, interactive control of interaction terms via an allowed matrix, comprehensive model diagnostics with variable importance and partial dependence plots, and publication-quality report generation via Quarto'.

The purpose of this package is to support the setup the R environment. The two main features are autos', to automatically source files and/or directories into your environment, and paths to consistently set path objects across projects for input and output. Both are implemented using a configuration file to allow easy, custom configurations that can be used for multiple or all projects.

The R4EPIs project <https://r4epi.github.io/sitrep/> seeks to provide a set of standardized tools for analysis of outbreak and survey data in humanitarian aid settings. This package currently provides standardized data dictionaries from Medecins Sans Frontieres Operational Centre Amsterdam for outbreak scenarios (Acute Jaundice Syndrome, Cholera, Diphtheria, Measles, Meningitis) and surveys (Retrospective mortality and access to care, Malnutrition, Vaccination coverage and Event Based Surveillance) - as described in the following <https://scienceportal.msf.org/assets/standardised-mortality-surveys?utm_source=chatgpt.com>. In addition, a data generator from these dictionaries is provided. It is also possible to read in any Open Data Kit format data dictionary.

Presents two methods to estimate the parameters mu', sigma', and tau of an ex-Gaussian distribution. Those methods are Quantile Maximization Likelihood Estimation ('QMLE') and Bayesian. The QMLE method allows a choice between three different estimation algorithms for these parameters : neldermead ('NEMD'), fminsearch ('FMIN'), and nlminb ('NLMI'). For more details about the methods you can refer at the following list: Brown, S., & Heathcote, A. (2003) <doi:10.3758/BF03195527>; McCormack, P. D., & Wright, N. M. (1964) <doi:10.1037/h0083285>; Van Zandt, T. (2000) <doi:10.3758/BF03214357>; El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021) <doi:10.19139/soic-2310-5070-1251>; Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995) <doi:10.2307/2986138>.

Collection of ancillary functions and utilities for Partial Linear Single Index Models for Environmental mixture analyses, which currently provides functions for scalar outcomes. The outputs of these functions include the single index function, single index coefficients, partial linear coefficients, mixture overall effect, exposure main and interaction effects, and differences of quartile effects. In the future, we will add functions for binary, ordinal, Poisson, survival, and longitudinal outcomes, as well as models for time-dependent exposures. See Wang et al (2020) <doi:10.1186/s12940-020-00644-4> for an overview.

The EXPOS model uses a digital elevation model (DEM) to estimate exposed and protected areas for a given hurricane wind direction and inflection angle. The resulting topographic exposure maps can be combined with output from the HURRECON model to estimate hurricane wind damage across a region. For details on the original version of the EXPOS model written in Borland Pascal', see: Boose, Foster, and Fluet (1994) <doi:10.2307/2937142>, Boose, Chamberlin, and Foster (2001) <doi:10.1890/0012-9615(2001)071[0027:LARIOH]2.0.CO;2>, and Boose, Serrano, and Foster (2004) <doi:10.1890/02-4057>.

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>.

Comprehensive toolkit for addressing selection bias in binary disease models across diverse non-probability samples, each with unique selection mechanisms. It utilizes Inverse Probability Weighting (IPW) and Augmented Inverse Probability Weighting (AIPW) methods to reduce selection bias effectively in multiple non-probability cohorts by integrating data from either individual-level or summary-level external sources. The package also provides a variety of variance estimation techniques. Please refer to Kundu et al. <doi:10.48550/arXiv.2412.00228>.

This package provides a collection of curated educational datasets for teaching ecology and agriculture concepts. Includes data on wildlife monitoring, plant treatments, and ecological observations with documentation and examples for educational use. All datasets are derived from published scientific studies and are available under CC0 or compatible licenses.

Support for measurement errors in R vectors, matrices and arrays: automatic uncertainty propagation and reporting. Documentation about errors is provided in the paper by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in this package as a vignette; see citation("errors") for details.

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 provides tools for automatic model selection and diagnostics for Climate and Environmental data. In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models. The envcpt() function should be your first port of call.

Ever read or wrote source files containing sectioning comments? If these comments are markdown style section comments, you can excerpt them and set a table of contents using the python package excerpts (<https://pypi.org/project/excerpts/>).

This package provides a flexible framework for Agent-Based Models (ABM), the epiworldR package provides methods for prototyping disease outbreaks and transmission models using a C++ backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents features, providing great complexity for the model dynamics. Furthermore, epiworldR is ideal for simulation studies featuring large populations.

This package provides methods for data analysis from an entropic perspective. These methods are nonparametric and perform well on non-ordinal data. Currently includes HeatMap() for visualizing distributional characteristics among multiple populations (groups).

Access and interrogate EMODnet (European Marine Observation and Data Network) Web Feature Service data <https://emodnet.ec.europa.eu/en/emodnet-web-service-documentation#data-download-services>. This includes listing existing data sources, and getting data from each of them.

This package provides functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) <DOI:10.1109/TKDE.2012.50>, <DOI:10.1016/j.ejor.2014.04.001>.

An ensemble method for the statistical detection of a rare class in two-class classification problems. The method uses an ensemble of classifiers where the constituent models of the ensemble use disjoint subsets (phalanxes) of explanatory variables. We provide an implementation of the phalanx-formation algorithm. Please see Tomal et al. (2015) <doi:10.1214/14-AOAS778>, Tomal et al. (2016) <doi:10.1021/acs.jcim.5b00663>, and Tomal et al. (2019) <arXiv:1706.06971> for more details.

Easily create interactive charts by leveraging the Echarts Javascript library which includes 36 chart types, themes, Shiny proxies and animations.

Finding life outside the planet Earth several is the ultimate goal of an astrobiologist. Using known astronomical measurements and assumptions the probability of extraterrestrial life existence could be estimated. Equations such as the Drake equation (1961) as stated in the paper of Molina (2019) <arXiv:1912.01783>, Seager (2013) <https://www.space.com/22648-drake-equation-alien-life-seager.html> and Foucher et al, (2017) <doi:10.3390/life7040040> are included in the extraterrestrial package.

This package provides statistical and visualization tools for the analysis of demographic indicators, and spatio-temporal behavior and characterization of outbreaks of vector-borne diseases (VBDs) in Colombia. It implements travel times estimated in Bravo-Vega C., Santos-Vega M., & Cordovez J.M. (2022), and the endemic channel method (Bortman, M. (1999) <https://iris.paho.org/handle/10665.2/8562>).

Simulates and estimates the Exponential Random Partition Model presented in the paper Hoffman, Block, and Snijders (2023) <doi:10.1177/00811750221145166>. It can also be used to estimate longitudinal partitions, following the model proposed in Hoffman and Chabot (2023) <doi:10.1016/j.socnet.2023.04.002>. The model is an exponential family distribution on the space of partitions (sets of non-overlapping groups) and is called in reference to the Exponential Random Graph Models (ERGM) for networks.

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'.

This package provides functions supporting the reading and parsing of internal e-book content from EPUB files. The epubr package provides functions supporting the reading and parsing of internal e-book content from EPUB files. E-book metadata and text content are parsed separately and joined together in a tidy, nested tibble data frame. E-book formatting is not completely standardized across all literature. It can be challenging to curate parsed e-book content across an arbitrary collection of e-books perfectly and in completely general form, to yield a singular, consistently formatted output. Many EPUB files do not even contain all the same pieces of information in their respective metadata. EPUB file parsing functionality in this package is intended for relatively general application to arbitrary EPUB e-books. However, poorly formatted e-books or e-books with highly uncommon formatting may not work with this package. There may even be cases where an EPUB file has DRM or some other property that makes it impossible to read with epubr'. Text is read as is for the most part. The only nominal changes are minor substitutions, for example curly quotes changed to straight quotes. Substantive changes are expected to be performed subsequently by the user as part of their text analysis. Additional text cleaning can be performed at the user's discretion, such as with functions from packages like tm or qdap'.