Simple Component Analysis (SCA) often provides much more interpretable components than Principal Components (PCA) while still representing much of the variability in the data.

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>.

Build custom Europe SpatialPolygonsDataFrame, if you don't know what is a SpatialPolygonsDataFrame see SpatialPolygons() in sp', by example for mapLayout() in antaresViz'. Antares is a powerful software developed by RTE to simulate and study electric power systems (more information about Antares here: <https://antares-simulator.org/>).

This package provides a step-down procedure for controlling the False Discovery Proportion (FDP) in a competition-based setup, implementing Dong et al. (2020) <arXiv:2011.11939>. Such setups include target-decoy competition (TDC) in computational mass spectrometry and the knockoff construction in linear regression.

Includes four functions: RFactor_calc(), RFactor_est(), KFactor() and SoilLoss(). The rainfall erosivity factors can be calculated or estimated, and soil erodibility will be estimated by the equation extracted from the monograph. Soil loss will be estimated by the product of five factors (rainfall erosivity, soil erodibility, length and steepness slope, cover-management factor and support practice factor. In the future, additional functions can be included. This efforts to advance research in soil and water conservation, with fast and accurate results.

Dictionary-like reference for computing scoring rules in a wide range of situations. Covers both parametric forecast distributions (such as mixtures of Gaussians) and distributions generated via simulation. Further details can be found in the package vignettes <doi:10.18637/jss.v090.i12>, <doi:10.18637/jss.v110.i08>.

Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) <http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf>.

Implementation of the Conditional Least Square (CLS) estimates and its covariance matrix for the first-order spatial integer-valued autoregressive model (SINAR(1,1)) proposed by Ghodsi (2012) <doi:10.1080/03610926.2011.560739>.

Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on kohonen and openair packages implemented by functions embedding Vesanto et al. (2001) <http://www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf> heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) <doi:10.4209/aaqr.2019.08.0414> are provided.

This package provides a set of tools for writing and sharing interactive courses to be used with swirl.

This package provides tools for applying Sklar's Omega (Hughes, 2022) <doi:10.1007/s11222-022-10105-2> methodology to nominal scores, ordinal scores, percentages, counts, amounts (i.e., non-negative real numbers), and balances (i.e., any real number). The framework can accommodate any number of units, any number of coders, and missingness; and can be used to measure agreement with a gold standard, intra-coder agreement, and/or inter-coder agreement. Frequentist inference is supported for all levels of measurement. Bayesian inference is supported for continuous scores only.

Estimate the receiver operating characteristic (ROC) curve, area under the curve (AUC) and optimal cut-off points for individual classification taking into account complex sampling designs when working with complex survey data. Methods implemented in this package are described in: A. Iparragirre, I. Barrio, I. Arostegui (2024) <doi:10.1002/sta4.635>; A. Iparragirre, I. Barrio, J. Aramendi, I. Arostegui (2022) <doi:10.2436/20.8080.02.121>; A. Iparragirre, I. Barrio (2024) <doi:10.1007/978-3-031-65723-8_7>.

These functions were developed within SECFISH project (Strengthening regional cooperation in the area of fisheries data collection-Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level). They are aimed at identifying correlations between costs and transversal variables by metier using individual vessel data and for disaggregating variable costs from fleet segment to metier level.

This package provides tools to calculate the alpha parameter of the Weibull distribution, given beta and the age-specific fertility of a species, so that the population remains stable and stationary. Methods are inspired by "Survival profiles from linear models versus Weibull models: Estimating stable and stationary population structures for Pleistocene large mammals" (Martà n-González et al. 2019) <doi:10.1016/j.jasrep.2019.03.031>.

Surveys to collect employment data so as to obtain data estimates on the number of employed people, the number of unemployed, and other employment indicators.

Starting from a Regression Model, it provides a stepwise procedure to select the linear predictor.

Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.

Spatio-temporal change of support (STCOS) methods are designed for statistical inference on geographic and time domains which differ from those on which the data were observed. In particular, a parsimonious class of STCOS models supporting Gaussian outcomes was introduced by Bradley, Wikle, and Holan <doi:10.1002/sta4.94>. The stcos package contains tools which facilitate use of STCOS models.

Visualizes sulcal morphometry data derived from BrainVisa <https://brainvisa.info/> including width, depth, surface area, and length. The package enables mapping of statistical group results or subject-level values onto cortical surface maps, with options to focus on all sulci or only selected regions of interest. Users can display all four measures simultaneously or restrict plots to chosen measures, creating composite, publication-quality brain visualizations in R to support the analysis and interpretation of sulcal morphology.

Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>.

Allows the user to connect with IBGE's (Instituto Brasileiro de Geografia e Estatistica, see <https://www.ibge.gov.br/> for more information) SIDRA API in a flexible way. SIDRA is the acronym to "Sistema IBGE de Recuperacao Automatica" and is the system where IBGE turns available aggregate data from their researches.

This package provides functions for converting and processing network data from a SpatialLinesDataFrame -Class object to an igraph'-Class object.

Hierarchical models for the analysis of species-area relationships (SARs) by combining several data sets and covariates; with a global data set combining individual SAR studies; as described in Solymos and Lele (2012) <doi:10.1111/j.1466-8238.2011.00655.x>.

Series of algorithms to translate users mental models of seascapes, landscapes and, more generally, of geographic features into computer representations (classifications). Spaces and geographic objects are classified with user-defined rules taking into account spatial data as well as spatial relationships among different classes and objects.