This package creates classifier for binary outcomes using Adaptive Boosting (AdaBoost) algorithm on decision stumps with a fast C++ implementation. For a description of AdaBoost, see Freund and Schapire (1997) <doi:10.1006/jcss.1997.1504>. This type of classifier is nonlinear, but easy to interpret and visualize. Feature vectors may be a combination of continuous (numeric) and categorical (string, factor) elements. Methods for classifier assessment, predictions, and cross-validation also included.

Using principal component analysis as a base model, SCOUTer offers a new approach to simulate outliers in a simple and precise way. The user can generate new observations defining them by a pair of well-known statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2) statistics. Just by introducing the target values of the SPE and T^2, SCOUTer returns a new set of observations with the desired target properties. Authors: Alba González, Abel Folch-Fortuny, Francisco Arteaga and Alberto Ferrer (2020).

An analytic framework for the calculation of norm- and criterion-referenced academic growth estimates using large scale, longitudinal education assessment data as developed in Betebenner (2009) <doi:10.1111/j.1745-3992.2009.00161.x>.

Estimate attribute values for one set of polygons from values measured on a different, misaligned set. Provides area-weighted areal interpolation and a dasymetric method that distributes values across a point layer (such as parcel centroids). Count (extensive) measures are total-preserving; rate (intensive) measures use area-weighted means.

Estimate the four parameters of stable laws using maximum likelihood method, generalised method of moments with finite and continuum number of points, iterative Koutrouvelis regression and Kogon-McCulloch method. The asymptotic properties of the estimators (covariance matrix, confidence intervals) are also provided.

Remove batch effects by projecting query batches into the reference batch space.

This package provides R bindings for the Stencila Schema <https://schema.stenci.la>. This package is primarily aimed at R developers wanting to programmatically generate, or modify, executable documents.

The stochastic (also called on-line) version of the Self-Organising Map (SOM) algorithm is provided. Different versions of the algorithm are implemented, for numeric and relational data and for contingency tables as described, respectively, in Kohonen (2001) <isbn:3-540-67921-9>, Olteanu & Villa-Vialaneix (2005) <doi:10.1016/j.neucom.2013.11.047> and Cottrell et al (2004) <doi:10.1016/j.neunet.2004.07.010>. The package also contains many plotting features (to help the user interpret the results), can handle (and impute) missing values and is delivered with a graphical user interface based on shiny'.

Extends the functionality of the package Synth as detailed in Abadie, Diamond, and Hainmueller (2011) <doi:10.18637/jss.v042.i13>. Includes generating and plotting placebos, post/pre-MSPE (Mean Squared Prediction Error) significance tests and plots, and calculating average treatment effects for multiple treated units.

This package provides standard load profiles (SLPs) for electricity and gas published by the German Association of Energy and Water Industries (BDEW Bundesverband der Energie- und Wasserwirtschaft e.V.) in a tidy format. The electricity profiles cover the 1999 profiles â households (H0), commercial (G0â G6), and agriculture (L0â L2) â and the updated 2025 profiles (H25, G25, L25, P25, S25), which additionally represent households with photovoltaic systems and battery storage. An interface generates an electricity load profile over a user-defined date range. A second interface generates daily standard load profiles for gas using the synthetic SigLinDe method. More information on the data and methodology for electricity is described in "Standardlastprofile Strom", <https://www.bdew.de/energie/standardlastprofile-strom/>, and for gas in "Standardlastprofile Gas", <https://www.bdew.de/energie/standardlastprofile-gas/>.

An end-to-end toolkit for land use and land cover classification using big Earth observation data. Builds satellite image data cubes from cloud collections. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Enables merging of multi-source imagery (SAR, optical, DEM). Includes functions for quality assessment of training samples using self-organized maps and to reduce training samples imbalance. Provides machine learning algorithms including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolution neural networks, and temporal attention encoders. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference. Enables best practices for estimating area and assessing accuracy of land change. Includes object-based spatio-temporal segmentation for space-time OBIA. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Datasets and functions for the book "Statistiques pour lâ économie et la gestion", "Théorie et applications en entreprise", F. Bertrand, Ch. Derquenne, G. Dufrénot, F. Jawadi and M. Maumy, C. Borsenberger editor, (2021, ISBN:9782807319448, De Boeck Supérieur, Louvain-la-Neuve). The first chapter of the book is dedicated to an introduction to statistics and their world. The second chapter deals with univariate exploratory statistics and graphics. The third chapter deals with bivariate and multivariate exploratory statistics and graphics. The fourth chapter is dedicated to data exploration with Principal Component Analysis. The fifth chapter is dedicated to data exploration with Correspondance Analysis. The sixth chapter is dedicated to data exploration with Multiple Correspondance Analysis. The seventh chapter is dedicated to data exploration with automatic clustering. The eighth chapter is dedicated to an introduction to probability theory and classical probability distributions. The ninth chapter is dedicated to an estimation theory, one-sample and two-sample tests. The tenth chapter is dedicated to an Gaussian linear model. The eleventh chapter is dedicated to an introduction to time series. The twelfth chapter is dedicated to an introduction to probit and logit models. Various example datasets are shipped with the package as well as some new functions.

This package provides a collection of sample datasets on various fields such as automotive performance and safety data to historical demographics and socioeconomic indicators, as well as recreational data. It serves as a resource for researchers and analysts seeking to perform analyses and derive insights from classic data sets in R.

Generates a random quotation from a database of quotes on topics in statistics, data visualization and science. Other functions allow searching the quotes database by key term tags, or authors or creating a word cloud. The output is designed to be suitable for use at the console, in Rmarkdown and LaTeX.

This package provides a modern, S7'-based foundation for survey analysis spanning both probability and non-probability samples. Probability sample designs include Taylor series linearization, replicate weights (BRR, Fay, jackknife, bootstrap), and two-phase estimation, following Lumley (2004) <doi:10.18637/jss.v009.i08>. Non-probability sample designs support bootstrap and jackknife variance estimation for opt-in panels and convenience samples. Provides a unified estimator interface for means, frequencies, totals, quantiles, ratios, correlations, regression, and t-tests, with weighted polychoric and polyserial correlation following Mannan (2025) <doi:10.2139/ssrn.6580480>. A metadata system preserves haven'-style variable labels, value labels, and question-preface attributes through all operations. Uses a tidyselect interface throughout.

It helps in determination of sample size for estimating population mean or proportion under simple random sampling with or without replacement and stratified random sampling without replacement. When prior information on the population coefficient of variation (CV) is unavailable, then a preliminary sample is drawn to estimate the CV which is used to compute the final sample size. If the final size exceeds the preliminary sample size, then additional units are drawn; otherwise, the preliminary sample size is considered as final sample size. For stratified random sampling without replacement design, it also calculates the sample size in each stratum under different allocation methods for estimation of population mean and proportion based upon the availability of prior information on sizes of the strata, standard deviations of the strata and costs of drawing a sampling unit in the strata.For details on sampling methodology, see, Cochran (1977) "Sampling Techniques" <https://archive.org/details/samplingtechniqu0000coch_t4x6>.

Execute the self-controlled case series (SCCS) design using observational data in the OMOP Common Data Model. Extracts all necessary data from the database and transforms it to the format required for SCCS. Age and season can be modeled using splines assuming constant hazard within calendar months. Event-dependent censoring of the observation period can be corrected for. Many exposures can be included at once (MSCCS), with regularization on all coefficients except for the exposure of interest. Includes diagnostics for all major assumptions of the SCCS.

Estimates area and subarea level proportions using the Small Area Estimation (SAE) Twofold Subarea Model with a hierarchical Bayesian (HB) approach under Beta distribution. A number of simulated datasets generated for illustration purposes are also included. The rstan package is employed to estimate parameters via the Hamiltonian Monte Carlo and No U-Turn Sampler algorithm. The model-based estimators include the HB mean, the variation of the mean, and quantiles. For references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Torabi and Rao (2014) <doi:10.1016/j.jmva.2014.02.001>, Leyla Mohadjer et al.(2007) <http://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000559.pdf>, Erciulescu et al.(2019) <doi:10.1111/rssa.12390>, and Yudasena (2024).

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This package provides a-priori, post-hoc, and compromise power-analyses for structural equation models (SEM).

Input/Output, processing and visualization of spectra taken with different spectrometers, including SVC (Spectra Vista), ASD and PSR (Spectral Evolution). Implements an S3 class spectra that other packages can build on. Provides methods to access, plot, manipulate, splice sensor overlap, vector normalize and smooth spectra.

The straightforward filtering index (SFINX) identifies true positive protein interactions in a fast, user-friendly, and highly accurate way. It is not only useful for the filtering of affinity purification - mass spectrometry (AP-MS) data, but also for similar types of data resulting from other co-complex interactomics technologies, such as TAP-MS, Virotrap and BioID. SFINX can also be used via the website interface at <http://sfinx.ugent.be>.

For a single, known pathogen phylogeny, provides functions for enumeration of the set of compatible epidemic transmission trees, and for uniform sampling from that set. Optional arguments allow for incomplete sampling with a known number of missing individuals, multiple sampling, and known infection time limits. Always assumed are a complete transmission bottleneck and no superinfection or reinfection. See Hall and Colijn (2019) <doi:10.1093/molbev/msz058> for methodology.

In practice, it is difficult to determine the number of decomposition modes, K, for Variational Mode Decomposition (VMD). To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021)<doi:10.1016/j.aej.2021.01.055>.