Set of tools for peak filtering of mass spectrometry imaging data based on spatial distribution of signal. Given a region-of-interest, representing the spatial region where the informative signal is expected to be localized, a series of filters determine which peak signals are characterized by an implausible spatial distribution. The filters reduce the dataset dimension and increase its information vs noise ratio, improving the quality of the unsupervised analysis results, reducing data dimension and simplifying the chemical interpretation. The methods are described in Inglese P. et al (2019) <doi:10.1093/bioinformatics/bty622>.

This package provides statistical process control tools for stochastic textured surfaces. The current version supports the following tools: (1) generic modeling of stochastic textured surfaces. (2) local defect monitoring and diagnostics in stochastic textured surfaces, which was proposed by Bui and Apley (2018a) <doi:10.1080/00401706.2017.1302362>. (3) global change monitoring in the nature of stochastic textured surfaces, which was proposed by Bui and Apley (2018b) <doi:10.1080/00224065.2018.1507559>. (4) computation of dissimilarity matrix of stochastic textured surface images, which was proposed by Bui and Apley (2019b) <doi:10.1016/j.csda.2019.01.019>.

The speckle package contains functions for the analysis of single cell RNA-seq data. The speckle package currently contains functions to analyse differences in cell type proportions. There are also functions to estimate the parameters of the Beta distribution based on a given counts matrix, and a function to normalise a counts matrix to the median library size. There are plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts. As our research into specialised analyses of single cell data continues we anticipate that the package will be updated with new functions.

Fits Bayesian hierarchical spatial process models for point-referenced Gaussian, Poisson, binomial, and binary data using stacking of predictive densities. It involves sampling from analytically available posterior distributions conditional upon some candidate values of the spatial process parameters and, subsequently assimilate inference from these individual posterior distributions using Bayesian predictive stacking. Our algorithm is highly parallelizable and hence, much faster than traditional Markov chain Monte Carlo algorithms while delivering competitive predictive performance. See Zhang, Tang, and Banerjee (2024) <doi:10.48550/arXiv.2304.12414>, and, Pan, Zhang, Bradley, and Banerjee (2024) <doi:10.48550/arXiv.2406.04655> for details.

This package performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) <doi:10.1007/s10651-007-0035-y>. The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package.

This package provides a collection of functions for estimating spatial and spatio-temporal regression models. Moran eigenvectors are used as spatial basis functions to efficiently approximate spatially dependent Gaussian processes (i.e., random effects eigenvector spatial filtering; see Murakami and Griffith 2015 <doi: 10.1007/s10109-015-0213-7>). The implemented models include linear regression with residual spatial dependence, spatially/spatio-temporally varying coefficient models (Murakami et al., 2017, 2024; <doi:10.1016/j.spasta.2016.12.001>,<doi:10.48550/arXiv.2410.07229>), spatially filtered unconditional quantile regression (Murakami and Seya, 2019 <doi:10.1002/env.2556>), Gaussian and non-Gaussian spatial mixed models through compositionally-warping (Murakami et al. 2021, <doi:10.1016/j.spasta.2021.100520>).

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.

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>.

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>, Garcà a-Portugués et al. (2024) <doi:10.48550/arXiv.2108.09874>, and Fernández-de-Marcos and Garcà a-Portugués (2024) <doi:10.48550/arXiv.2405.13531>.

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.

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

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

This package implements the sparse biclustering proposal of Tan and Witten (2014), Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics 23(4):985-1008.

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.

Latent space models for multivariate networks (multiplex) estimated via MCMC algorithm. See D Angelo et al. (2018) <arXiv:1803.07166> and D Angelo et al. (2018) <arXiv:1807.03874>.

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.