This package provides semiparametric smooth-coefficient stochastic frontier analysis following Sun and Kumbhakar (2013) <doi:10.1016/j.econlet.2013.05.001> where the coefficients of the parametric part vary smoothly with a set of nonparametric variables. Inefficiency term is allowed to depend on a set of determinants through heteroskedasticity. Smooth coefficients are estimated using nonparametric regression and the remaining frontier parameters are estimated by maximum likelihood. Technical efficiency and inefficiency are computed using the Battese and Coelli (1988) <doi:10.1016/0304-4076(88)90053-X> and Jondrow et al. (1982) <doi:10.1016/0304-4076(82)90004-5> methods, respectively. Confidence intervals for technical efficiency are computed using the approach of Horrace and Schmidt (1996) <doi:10.1007/BF00157044>.

An implementation of ranked sparsity methods, including penalized regression methods such as the sparsity-ranked lasso, its non-convex alternatives, and elastic net, as well as the sparsity-ranked Bayesian Information Criterion. As described in Peterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked sparsity is a philosophy with methods primarily useful for variable selection in the presence of prior informational asymmetry, which occurs in the context of trying to perform variable selection in the presence of interactions and/or polynomials. Ultimately, this package attempts to facilitate dealing with cumbersome interactions and polynomials while not avoiding them entirely. Typically, models selected under ranked sparsity principles will also be more transparent, having fewer falsely selected interactions and polynomials than other methods.

This package implements algorithms for terrestrial, mobile, and airborne lidar processing, tree detection, segmentation, and attribute estimation (Donager et al., 2021) <doi:10.3390/rs13122297>, and a hierarchical patch delineation algorithm PatchMorph (Girvetz & Greco, 2007) <doi:10.1007/s10980-007-9104-8>. Tree detection uses rasterized point cloud metrics (relative neighborhood density and verticality) combined with RANSAC cylinder fitting to locate tree boles and estimate diameter at breast height. Tree segmentation applies graph-theory approaches inspired by Tao et al. (2015) <doi:10.1016/j.isprsjprs.2015.08.007> with cylinder fitting methods from de Conto et al. (2017) <doi:10.1016/j.compag.2017.07.019>. PatchMorph delineates habitat patches across spatial scales using organism-specific thresholds. Built on lidR (Roussel et al., 2020) <doi:10.1016/j.rse.2020.112061>.

The notion of spreading activation is a prevalent metaphor in the cognitive sciences. This package provides the tools for cognitive scientists and psychologists to conduct computer simulations that implement spreading activation in a network representation. The algorithmic method implemented in spreadr subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (2011, Frontiers), who viewed activation as a fixed cognitive resource that could spread among nodes that were connected to each other via edges or connections (i.e., a network). See Vitevitch, M. S., Ercal, G., & Adagarla, B. (2011). Simulating retrieval from a highly clustered network: Implications for spoken word recognition. Frontiers in Psychology, 2, 369. <doi:10.3389/fpsyg.2011.00369> and Siew, C. S. Q. (2019). spreadr: A R package to simulate spreading activation in a network. Behavior Research Methods, 51, 910-929. <doi: 10.3758/s13428-018-1186-5>.

This package provides a set of functions allowing to implement the SpiceFP approach which is iterative. It involves transformation of functional predictors into several candidate explanatory matrices (based on contingency tables), to which relative edge matrices with contiguity constraints are associated. Generalized Fused Lasso regression are performed in order to identify the best candidate matrix, the best class intervals and related coefficients at each iteration. The approach is stopped when the maximal number of iterations is reached or when retained coefficients are zeros. Supplementary functions allow to get coefficients of any candidate matrix or mean of coefficients of many candidates. The methods in this package are describing in Girault Gnanguenon Guesse, Patrice Loisel, Bénedicte Fontez, Thierry Simonneau, Nadine Hilgert (2021) "An exploratory penalized regression to identify combined effects of functional variables -Application to agri-environmental issues" <https://hal.archives-ouvertes.fr/hal-03298977>.

Offers a rich and diverse collection of datasets focused on sports, athletics, physical performance, and related disciplines. The package includes professional and amateur sports data covering team sports such as soccer, basketball, baseball, American football, volleyball, rugby, cricket, hockey, and handball, as well as individual sports including tennis, badminton, table tennis, golf, swimming, cycling, athletics, gymnastics, wrestling, boxing, martial arts, weightlifting, triathlon, rowing, canoeing, climbing, surfing, skiing, snowboarding, and motorsports. Datasets cover player and team performance, match statistics, tournament results, championship standings, Olympic and international competitions, rankings, player demographics, coaching and training, biomechanics, sports medicine, injuries, exercise physiology, fitness assessment, sports nutrition, wearable sensor measurements, talent identification, and sports analytics. Additional datasets include historical competitions, referee decisions, fan engagement, economic indicators, and sports management data obtained from public repositories, official organizations, research publications, and educational resources. Designed for sports scientists, coaches, analysts, researchers, educators, students, and data scientists, this package facilitates exploratory data analysis, statistical modeling, machine learning, visualization, and sports analytics research.

Modeling spatial dependencies in dependent variables, extending traditional spatial regression approaches. It allows for the joint modeling of both the mean and the variance of the dependent variable, incorporating semiparametric effects in both models. Based on generalized additive models (GAM), the package enables the inclusion of non-parametric terms while maintaining the classical theoretical framework of spatial regression. Additionally, it implements the Generalized Spatial Autoregression (GSAR) model, which extends classical methods like logistic Spatial Autoregresive Models (SAR), probit Spatial Autoregresive Models (SAR), and Poisson Spatial Autoregresive Models (SAR), offering greater flexibility in modeling spatial dependencies and significantly improving computational efficiency and the statistical properties of the estimators. Related work includes: a) J.D. Toloza-Delgado, Melo O.O., Cruz N.A. (2024). "Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices". <doi:10.1016/j.spasta.2024.100864>. b) Cruz, N. A., Toloza-Delgado, J. D., Melo, O. O. (2024). "Generalized spatial autoregressive model". <doi:10.48550/arXiv.2412.00945>.

Implementation of uniformity tests on the circle and (hyper)sphere. The main function of the package is unif_test(), which conveniently collects more than 35 tests for assessing uniformity on S^p-1 = x in R^p : ||x|| = 1, p >= 2. The test statistics are implemented in the unif_stat() function, which allows computing several statistics for different samples within a single call, thus facilitating Monte Carlo experiments. Furthermore, the unif_stat_MC() function allows parallelizing them in a simple way. The asymptotic null distributions of the statistics are available through the function unif_stat_distr(). The core of sphunif is coded in C++ by relying on the Rcpp package. The package also provides several novel datasets and gives the replicability for the data applications/simulations in Garcà a-Portugués et al. (2021) <doi:10.1007/978-3-030-69944-4_12>, Garcà a-Portugués et al. (2023) <doi:10.3150/21-BEJ1454>, Fernández-de-Marcos and Garcà a-Portugués (2024) <doi:10.1016/j.spl.2024.110218>, and Garcà a-Portugués et al. (2025) <doi:10.1080/01621459.2025.2566414>.

Processing and analysis of field collected or simulated sprinkler system catch data (depths) to characterize irrigation uniformity and efficiency using standard and other measures. Standard measures include the Christiansen coefficient of uniformity (CU) as found in Christiansen, J.E.(1942, ISBN:0138779295, "Irrigation by Sprinkling"); and distribution uniformity (DU), potential efficiency of the low quarter (PELQ), and application efficiency of the low quarter (AELQ) that are implementations of measures of the same notation in Keller, J. and Merriam, J.L. (1978) "Farm Irrigation System Evaluation: A Guide for Management" <https://pdf.usaid.gov/pdf_docs/PNAAG745.pdf>. spreval::DU.lh is similar to spreval::DU but is the distribution uniformity of the low half instead of low quarter as in DU. spreval::PELQT is a version of spreval::PELQ adapted for traveling systems instead of lateral move or solid-set sprinkler systems. The function spreval::eff is analogous to the method used to compute application efficiency for furrow irrigation presented in Walker, W. and Skogerboe, G.V. (1987,ISBN:0138779295, "Surface Irrigation: Theory and Practice"),that uses piecewise integration of infiltrated depth compared against soil-moisture deficit (SMD), when the argument "target" is set equal to SMD. The other functions contained in the package provide graphical representation of sprinkler system uniformity, and other standard univariate parametric and non-parametric statistical measures as applied to sprinkler system catch depths. A sample data set of field test data spreval::catchcan (catch depths) is provided and is used in examples and vignettes. Agricultural systems emphasized, but this package can be used for landscape irrigation evaluation, and a landscape (turf) vignette is included as an example application.

An algorithm to cluster satellite hot spot data spatially and temporally.

Generate syntax for use with the sparklines package for LaTeX.

The package contains functions that can be used to compare expression measures on different array platforms.

Data on the Spy vs. Spy comic strip of Mad magazine, created and written by Antonio Prohias.

This package provides tools for fitting linear models and generalized linear models to large data sets by updating algorithms.

Bulk RNA-seq from GTEx on 4,000 randomly selected, expressed genes. Data has been processed for co-expression analysis.

Scrap speech text and speaker informations of speeches of House of Representatives of Brazil, and transform in a cleaned tibble.

Bayesian inference for parametric proportional hazards spatial survival models; flexible spatial survival models. See Benjamin M. Taylor, Barry S. Rowlingson (2017) <doi:10.18637/jss.v077.i04>.

An R wrapper for pulling data from the Spotify Web API <https://developer.spotify.com/documentation/web-api/> in bulk, or post items on a Spotify user's playlist.

This package provides tools to analyze alternative splicing sites, interpret outcomes based on sequence information, select and design primers for site validiation and give visual representation of the event to guide downstream experiments.

This package provides a sparklyr <https://spark.posit.co/> extension that provides an R interface for XGBoost <https://github.com/dmlc/xgboost> on Apache Spark'. XGBoost is an optimized distributed gradient boosting library.

This package provides an interface to the SuiteSparse UMFPACK LU factorisation routines for sparse matrices stored in compressed column format. Implements the algorithm described in Davis (2004) <doi:10.1145/992200.992206>.

This package implements a generative model that uses a spike-and-slab like prior distribution obtained by multiplying a deterministic binary vector. Such a model allows an EM algorithm, optimizing a type-II log-likelihood.

This package provides tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.

This package constructs basis functions of B-splines, M-splines, I-splines, convex splines (C-splines), periodic splines, natural cubic splines, generalized Bernstein polynomials, their derivatives, and integrals (except C-splines) by closed-form recursive formulas. It also contains a C++ head-only library integrated with Rcpp.