Offers a set of functions to easily download and clean Brazilian electoral data from the Superior Electoral Court and CepespData websites. Among other features, the package retrieves data on local and federal elections for all positions (city councilor, mayor, state deputy, federal deputy, governor, and president) aggregated by state, city, and electoral zones.

This package implements EKIO's visual identity for data visualization with ggplot2'. Supplies a coordinated theme, color palettes, discrete and continuous scales, and helpers for accessible text contrast. Includes high-level recipes for common charts so analysts can create consistent, publication-ready graphics with concise code.

Downloads a satellite image via ESRI and maptiles (these are originally from a variety of aerial photography sources), translates the image into a perceptually uniform color space, runs one of a few different clustering algorithms on the colors in the image searching for a user-supplied number of colors, and returns the resulting color palette.

This is a data-only package containing model objects that predict human energy expenditure from wearable sensor data. Supported methods include the neural networks of Montoye et al. (2017) <doi:10.1080/1091367X.2017.1337638> and the models of Staudenmayer et al. (2015) <doi:10.1152/japplphysiol.00026.2015>, one a linear model and the other a random forest. The package is intended as a spoke for the hub-package accelEE', which brings together the above methods and others from packages such as Sojourn and TwoRegression.'.

Evaluates the performance of binary classifiers. Computes confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR, accuracy, F1, DOR, ..), and area under the curve. Outputs are well suited for nested dataframes.

Support functions for R-based "EQUALCompareImages - Compare similarity between and within images" shiny application which allow researchers without coding skills or expertise in image comparison algorithms to compare images. Gurusamy,K (2025)<doi:10.5281/zenodo.16994047>.

Null models to analyse ecological networks (e.g. food webs, flower-visitation networks, seed-dispersal networks) and detect resource preferences or non-random interactions among network nodes. Tools are provided to run null models, test for and plot preferences, plot and analyse bipartite networks, and export null model results in a form compatible with other network analysis packages. The underlying null model was developed by Agusti et al. (2003) Molecular Ecology <doi:10.1046/j.1365-294X.2003.02014.x> and the full application to ecological networks by Vaughan et al. (2018) econullnetr: an R package using null models to analyse the structure of ecological networks and identify resource selection. Methods in Ecology & Evolution, <doi:10.1111/2041-210X.12907>.

Data and analysis of ecological multilayer networks, including standardization of data structures and functions to convert between them. Includes an interactive multilayer network visualizer (beta, paper forthcoming), and a collection of 78 empirical ecological multilayer network datasets. This work was supported by research grant ISF (Israel Science Foundation) 1281/20 to Shai Pilosof. Noa Frydman (2023) <doi:10.1111/2041-210X.14225>.

This package provides classes and methods for implementing aquatic ecosystem models, for running these models, and for visualizing their results.

The encompassing test is developed based on multi-step-ahead predictions of two nested models as in Pitarakis, J. (2023) <doi:10.48550/arXiv.2312.16099>. The statistics are standardised to a normal distribution, and the null hypothesis is that the larger model contains no additional useful information. P-values will be provided in the output.

This package provides tools for generating hierarchical exploded-view maps from dense administrative boundary data. The package applies rigid-body translations to polygon geometries using a centroid-driven vector field, preserving the internal geometry of each feature while separating units within and across regions. Parameters can be derived analytically from dataset geometry using closed-form models for regional separation and local expansion. The package also includes grouped layouts, optional bounded collision refinement, and an interactive focus-map widget for selected-area inspection in htmlwidgets and Shiny'. It implements the methodology described in George Arthur (2026) <https://github.com/PrigasG/explodemap> "A Hierarchical Vector-Based Framework for Multi-Scale Exploded-View Cartography".

It enables detailed interpretation of complex classification and regression models through Shapley analysis including data-driven characterization of subgroups of individuals. Furthermore, it facilitates multi-measure model evaluation, model fairness, and decision curve analysis. Additionally, it offers enhanced visualizations with interactive elements.

Download and process public education data from INEP (Instituto Nacional de Estudos e Pesquisas Educacionais Anà sio Teixeira). Provides functions to access microdata from the School Census (Censo Escolar), ENEM (Exame Nacional do Ensino Médio), SAEB (Sistema de Avaliação da Educação Básica), Higher Education Census (Censo da Educação Superior), ENADE (Exame Nacional de Desempenho dos Estudantes), ENCCEJA (Exame Nacional para Certificação de Competências de Jovens e Adultos), IDD (Indicador de Diferença entre os Desempenhos Observado e Esperado), CPC (Conceito Preliminar de Curso), IGC (à ndice Geral de Cursos), CAPES graduate education data, FUNDEB (Fundo de Manutencao e Desenvolvimento da Educacao Basica), IDEB (à ndice de Desenvolvimento da Educação Básica), and other educational datasets. Returns data in tidy format ready for analysis. Data source: INEP Open Data Portal <https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos>.

In agricultural, post-harvest and processing, engineering and industrial experiments factors are often differentiated with ease with which they can change from experimental run to experimental run. This is due to the fact that one or more factors may be expensive or time consuming to change i.e. hard-to-change factors. These factors restrict the use of complete randomization as it may make the experiment expensive and time consuming. Split plot designs can be used for such situations. In general model estimation of split plot designs require the use of generalized least squares (GLS). However for some split-plot designs ordinary least squares (OLS) estimates are equivalent to generalized least squares (GLS) estimates. These types of designs are known in literature as equivalent-estimation split-plot design. For method details see, Macharia, H. and Goos, P.(2010) <doi:10.1080/00224065.2010.11917833>.Balanced split plot designs are designs which have an equal number of subplots within every whole plot. This package used to construct equivalent estimation balanced split plot designs for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function equivalent_BSPD().

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>).

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.

This package provides a comprehensive toolkit for single-cell annotation with the CellMarker2.0 database (see Xia Li, Peng Wang, Yunpeng Zhang (2023) <doi: 10.1093/nar/gkac947>). Streamlines biological label assignment in single-cell RNA-seq data and facilitates transcriptomic analysis, including preparation of TCGA<https://portal.gdc.cancer.gov/> and GEO<https://www.ncbi.nlm.nih.gov/geo/> datasets, differential expression analysis and visualization of enrichment analysis results. Additional utility functions support various bioinformatics workflows. See Wei Cui (2024) <doi: 10.1101/2024.09.14.609619> for more details.

The EUNIS habitat classification is a comprehensive pan-European system for habitat identification <https://www.eea.europa.eu/data-and-maps/data/eunis-habitat-classification-1>. This is an R data package providing the EUNIS classification system. The classification is hierarchical and covers all types of habitats from natural to artificial, from terrestrial to freshwater and marine. The habitat types are identified by specific codes, names and descriptions and come with schema crosswalks to other habitat typologies.

This package implements entropy balancing, a data preprocessing procedure described in Hainmueller (2012, <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. Useful for creating balanced samples in observational studies with a binary treatment where the control group is reweighted to match the covariate moments of the treatment group, and for reweighting a survey sample to known characteristics from a target population.

Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics as shown in Vega Yon et al. (2020) <DOI:10.1016/j.socnet.2020.07.005>. As a difference from the ergm package, ergmito circumvents using Markov-Chain Maximum Likelihood Estimator (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs for small networks. As exhaustive enumeration is computationally feasible for small networks, this R package takes advantage of this and provides tools for calculating likelihood functions, and other relevant functions, directly, meaning that in many cases both estimation and simulation of ERGMs for small networks can be faster and more accurate than simulation-based algorithms.

Fits the space-time Epidemic Type Aftershock Sequence ('ETAS') model to earthquake catalogs using a stochastic declustering approach. The ETAS model is a spatio-temporal marked point process model and a special case of the Hawkes process. The package is based on a Fortran program by Jiancang Zhuang (available at <https://bemlar.ism.ac.jp/zhuang/software.html>), which is modified and translated into C++ and C such that it can be called from R. Parallel computing with OpenMP is possible on supported platforms.

This package provides a conservative, assumption-aware statistical consistency checker for published research results. Parses test statistics, effect sizes, and confidence intervals from text, PDF, HTML, and Word documents across multiple citation styles including American Psychological Association (APA), Harvard, Frontiers, PLOS ONE, Scientific Reports, Nature Human Behaviour, PeerJ, eLife, PNAS, and others. Recomputes effect sizes using all plausible variants when design is ambiguous, and validates internal consistency. Supports t-tests, F-tests/ANOVA, correlations, chi-square, z-tests, regression, and nonparametric tests. Provides statcheck'-compatible API functions for batch processing of files and directories. Explicitly tracks all assumptions and uncertainty in output. Detects decision errors (significance reversals) similar to statcheck'. Note: this package is under active development and results should be independently verified. Use is at the sole responsibility of the user. Contributions and verification reports are welcome.

This package provides a set of functions to solve Erlang-C model. The Erlang C formula was invented by the Danish Mathematician A.K. Erlang and is used to calculate the number of advisors and the service level.

This package contains all data sets for Exam PA: Predictive Analytics at <https://exampa.net/>.