This package provides computational tools for working with the Extended Laplace distribution, including the probability density function, cumulative distribution function, quantile function, random variate generation based on convolution with Uniform noise and the quantile-quantile plot. Useful for modeling contaminated Laplace data and other applications in robust statistics. See Saah and Kozubowski (2025) <doi:10.1016/j.cam.2025.116588>.

Package computes and displays tables with support for SPSS'-style labels, multiple and nested banners, weights, multiple-response variables and significance testing. There are facilities for nice output of tables in knitr', Shiny', *.xlsx files, R and Jupyter notebooks. Methods for labelled variables add value labels support to base R functions and to some functions from other packages. Additionally, the package brings popular data transformation functions from SPSS Statistics and Excel': RECODE', COUNT', COUNTIF', VLOOKUP and etc. These functions are very useful for data processing in marketing research surveys. Package intended to help people to move data processing from Excel and SPSS to R.

Download data from the European Social Survey directly from their website <http://www.europeansocialsurvey.org/>. There are two families of functions that allow you to download and interactively check all countries and rounds available.

This package contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.

This package provides a tool for conducting exact parametric regression-based causal mediation analysis of binary outcomes as described in Samoilenko, Blais and Lefebvre (2018) <doi:10.1353/obs.2018.0013>; Samoilenko, Lefebvre (2021) <doi:10.1093/aje/kwab055>; and Samoilenko, Lefebvre (2023) <doi:10.1002/sim.9621>.

Extracting desired data using the proper Census variable names can be time-consuming. This package takes the pain out of that process by providing functions to quickly locate variables and download labeled tables from the Census APIs (<https://www.census.gov/data/developers/data-sets.html>).

Simulation of Electric Vehicles charging sessions using Gaussian models, together with time-series power demand calculations.

Package implements the EDNE-test for equivalence according to Hoffelder et al. (2015) <DOI:10.1080/10543406.2014.920344>. "EDNE" abbreviates "Euclidean Distance between the Non-standardized Expected values". The EDNE-test for equivalence is a multivariate two-sample equivalence test. Distance measure of the test is the Euclidean distance. The test is an asymptotically valid test for the family of distributions fulfilling the assumptions of the multivariate central limit theorem (see Hoffelder et al.,2015). The function EDNE.EQ() implements the EDNE-test for equivalence according to Hoffelder et al. (2015). The function EDNE.EQ.dissolution.profiles() implements a variant of the EDNE-test for equivalence analyses of dissolution profiles (see Suarez-Sharp et al.,2020 <DOI:10.1208/s12248-020-00458-9>). EDNE.EQ.dissolution.profiles() checks whether the quadratic mean of the differences of the expected values of both dissolution profile populations is statistically significantly smaller than 10 [\% of label claim]. The current regulatory standard approach for equivalence analyses of dissolution profiles is the similarity factor f2. The statistical hypotheses underlying EDNE.EQ.dissolution.profiles() coincide with the hypotheses for f2 (see Hoffelder et al.,2015, Suarez-Sharp et al., 2020).

Can be used to simultaneously estimate networks (Gaussian Graphical Models) in data from different groups or classes via Joint Graphical Lasso. Tuning parameters are selected via information criteria (AIC / BIC / extended BIC) or cross validation.

Model-based clustering for paired data based on the regression of a mixture of Bayesian hierarchical models on covariates. Zhang et al. (2023) <doi:10.1186/s12859-023-05556-x>.

This package implements two estimations related to the foundations of info metrics applied to ecological inference. These methodologies assess the lack of disaggregated data and provide an approach to obtaining disaggregated territorial-level data. For more details, see the following references: Fernández-Vázquez, E., Dà az-Dapena, A., Rubiera-Morollón, F. et al. (2020) "Spatial Disaggregation of Social Indicators: An Info-Metrics Approach." <doi:10.1007/s11205-020-02455-z>. Dà az-Dapena, A., Fernández-Vázquez, E., Rubiera-Morollón, F., & Vinuela, A. (2021) "Mapping poverty at the local level in Europe: A consistent spatial disaggregation of the AROPE indicator for France, Spain, Portugal and the United Kingdom." <doi:10.1111/rsp3.12379>.

We introduced a novel ensemble-based explainable machine learning model using Model Confidence Set (MCS) and two stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The model combined the predictive capabilities of different machine-learning models and integrates the interpretability of explainability methods. To develop the proposed algorithm, a two-stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) framework was employed. The package has been developed using the algorithm of Paul et al. (2023) <doi:10.1007/s40009-023-01218-x> and Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.

Highest averages & largest remainders allocating seats methods and several party system scores. Implemented highest averages allocating seats methods are D'Hondt, Webster, Danish, Imperiali, Hill-Huntington, Dean, Modified Sainte-Lague, equal proportions and Adams. Implemented largest remainders allocating seats methods are Hare, Droop, Hangenbach-Bischoff, Imperial, modified Imperial and quotas & remainders. The main advantage of this package is that ties are always reported and not incorrectly allocated. Party system scores provided are competitiveness, concentration, effective number of parties, party nationalization score, party system nationalization score and volatility. References: Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>. Norris (2004, ISBN:0-521-82977-1). Laakso & Taagepera (1979) <https://escholarship.org/uc/item/703827nv>. Jones & Mainwaring (2003) <https://kellogg.nd.edu/sites/default/files/old_files/documents/304_0.pdf>. Pedersen (1979) <https://janda.org/c24/Readings/Pedersen/Pedersen.htm>. Golosov (2010) <doi:10.1177/1354068809339538>. Golosov (2014) <doi:10.1177/1354068814549342>.

Analytical methods to locate and characterise ecotones, ecosystems and environmental patchiness along ecological gradients. Methods are implemented for isolated sampling or for space/time series. It includes Detrended Correspondence Analysis (Hill & Gauch (1980) <doi:10.1007/BF00048870>), fuzzy clustering (De Cáceres et al. (2010) <doi:10.1080/01621459.1963.10500845>), biodiversity indices (Jost (2006) <doi:10.1111/j.2006.0030-1299.14714.x>), and network analyses (Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>) - as well as tools to explore the number of clusters in the data. Functions to produce synthetic ecological datasets are also provided.

Essential Biodiversity Variables (EBV) are state variables with dimensions on time, space, and biological organization that document biodiversity change. Freely available ecosystem remote sensing products (ERSP) are downloaded and integrated with data for national or regional domains to derive indicators for EBV in the class ecosystem structure (Pereira et al., 2013) <doi:10.1126/science.1229931>, including horizontal ecosystem extents, fragmentation, and information-theory indices. To process ERSP, users must provide a polygon or geographic administrative data map. Downloadable ERSP include Global Surface Water (Peckel et al., 2016) <doi:10.1038/nature20584>, Forest Change (Hansen et al., 2013) <doi:10.1126/science.1244693>, and Continuous Tree Cover data (Sexton et al., 2013) <doi:10.1080/17538947.2013.786146>.

The goal of this package is to provide an easy to use, fast and scalable exhaustive search framework. Exhaustive feature selections typically require a very large number of models to be fitted and evaluated. Execution speed and memory management are crucial factors here. This package provides solutions for both. Execution speed is optimized by using a multi-threaded C++ backend, and memory issues are solved by by only storing the best results during execution and thus keeping memory usage constant.

Standardises and facilitates the use of eleven established stability properties that have been used to assess systemsâ responses to press or pulse disturbances at different ecological levels (e.g. population, community). There are two sets of functions. The first set corresponds to functions that measure stability at any level of organisation, from individual to community and can be applied to a time series of a systemâ s state variables (e.g., body mass, population abundance, or species diversity). The properties included in this set are: invariability, resistance, extent and rate of recovery, persistence, and overall ecological vulnerability. The second set of functions can be applied to Jacobian matrices. The functions in this set measure the stability of a community at short and long time scales. In the short term, the communityâ s response is measured by maximal amplification, reactivity and initial resilience (i.e. initial rate of return to equilibrium). In the long term, stability can be measured as asymptotic resilience and intrinsic stochastic invariability. Figueiredo et al. (2025) <doi:10.32942/X2M053>.

Reads EXIF data using ExifTool <https://exiftool.org> and returns results as a data frame. ExifTool is a platform-independent Perl library plus a command-line application for reading, writing and editing meta information in a wide variety of files. ExifTool supports many different metadata formats including EXIF, GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and ID3, as well as the maker notes of many digital cameras by Canon, Casio, FLIR, FujiFilm, GE, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung, Sanyo, Sigma/Foveon and Sony.

This software downloads and manages air quality data from the European Environmental Agency (EEA) dataflow (<https://www.eea.europa.eu/data-and-maps/data/aqereporting-9>). See the web page <https://eeadmz1-downloads-webapp.azurewebsites.net/> for details on the EEA's Air Quality Download Service. The package allows dynamically mapping the stations, summarising and time aggregating the measurements and building spatial interpolation maps. See the web page <https://www.eea.europa.eu/en> for further information on EEA activities and history. Further details, as well as, an extended vignette of the main functions included in the package, are available at the GitHub web page dedicated to the project.

Facilitates univariate and multivariate analysis of evolutionary sequences of phenotypic change. The package extends the modeling framework available in the paleoTS package. Please see <https://klvoje.github.io/evoTS/index.html> for information about the package and the implemented models.

Easily creating empirical distribution functions from data: dfun', pfun', qfun and rfun'.

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.

This package contains two functions that are intended to make tuning supervised learning methods easy. The eztune function uses a genetic algorithm or Hooke-Jeeves optimizer to find the best set of tuning parameters. The user can choose the optimizer, the learning method, and if optimization will be based on accuracy obtained through validation error, cross validation, or resubstitution. The function eztune.cv will compute a cross validated error rate. The purpose of eztune_cv is to provide a cross validated accuracy or MSE when resubstitution or validation data are used for optimization because error measures from both approaches can be misleading.

An R interface to United States Environmental Protection Agency (EPA) Environmental Compliance History Online ('ECHO') Application Program Interface (API). ECHO provides information about EPA permitted facilities, discharges, and other reporting info associated with permitted entities. Data are obtained from <https://echo.epa.gov/>.