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Package Name Access Summary Updated
r-spcavrp public Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2018) <arXiv:1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix. 2025-04-22
r-spcalda public A new reduced-rank LDA method which works for high dimensional multi-class data. 2025-04-22
r-spcadjust public Calibration of thresholds of control charts such as CUSUM charts based on past data, taking estimation error into account. 2025-04-22
r-spatialml public Implements a spatial extension of the random forest algorithm (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>). Future updates include more local machine learning methods as well as a geographically weighted random forest. 2025-04-22
r-spatialcovariance public Functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units. This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration. 2025-04-22
r-spatialacc public Provides a set of spatial accessibility measures from a set of locations (demand) to another set of locations (supply). It aims, among others, to support research on spatial accessibility to health care facilities. Includes the locations and some characteristics of major public hospitals in Greece. 2025-04-22
r-spatest public Performs score test using saddlepoint approximation to estimate the null distribution. Also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results. For the latest version, please check <https://github.com/leeshawn/SPAtest>. 2025-04-22
r-sparsestep public Implements the SparseStep model for solving regression problems with a sparsity constraint on the parameters. The SparseStep regression model was proposed in Van den Burg, Groenen, and Alfons (2017) <https://arxiv.org/abs/1701.06967>. In the model, a regularization term is added to the regression problem which approximates the counting norm of the parameters. By iteratively improving the approximation a sparse solution to the regression problem can be obtained. In this package both the standard SparseStep algorithm is implemented as well as a path algorithm which uses golden section search to determine solutions with different values for the regularization parameter. 2025-04-22
r-sparsepp public Provides interface to 'sparsepp' - fast, memory efficient hash map. It is derived from Google's excellent 'sparsehash' implementation. We believe 'sparsepp' provides an unparalleled combination of performance and memory usage, and will outperform your compiler's unordered_map on both counts. Only Google's 'dense_hash_map' is consistently faster, at the cost of much greater memory usage (especially when the final size of the map is not known in advance). 2025-04-22
r-sparsemvn public Computes multivariate normal (MVN) densities, and samples from MVN distributions, when the covariance or precision matrix is sparse. 2025-04-22
r-sparsemse public Implements the routines and algorithms developed and analysed in "Multiple systems estimation for Sparse Capture Data: Inferential Challenges when there are Non-Overlapping Lists" Chan, L, Silverman, B. W., Vincent, K (2019) <arXiv:1902.05156>. This package explicitly handles situations where there are pairs of lists which have no observed individuals in common. 2025-04-22
r-sparsemdc public Implements the algorithm described in Barron, M., and Li, J. (Not yet published). This algorithm clusters samples from multiple ordered populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseMDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers. 2025-04-22
r-sparseindextracking public Computation of sparse portfolios for financial index tracking, i.e., joint selection of a subset of the assets that compose the index and computation of their relative weights (capital allocation). The level of sparsity of the portfolios, i.e., the number of selected assets, is controlled through a regularization parameter. Different tracking measures are available, namely, the empirical tracking error (ETE), downside risk (DR), Huber empirical tracking error (HETE), and Huber downside risk (HDR). See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1, pp. 155-170, Jan. 2018. <doi:10.1109/TSP.2017.2762286>. 2025-04-22
r-sparsegrid public SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de 2025-04-22
r-sparseeigen public Computation of sparse eigenvectors of a matrix (aka sparse PCA) with running time 2-3 orders of magnitude lower than existing methods and better final performance in terms of recovery of sparsity pattern and estimation of numerical values. Can handle covariance matrices as well as data matrices with real or complex-valued entries. Different levels of sparsity can be specified for each individual ordered eigenvector and the method is robust in parameter selection. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the paper: K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on Signal Processing <doi:10.1109/TSP.2016.2605073>. 2025-04-22
r-sparsedc public Implements the algorithm described in Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data", Nucleic Acids Research, gkx1113, <doi:10.1093/nar/gkx1113>. This algorithm clusters samples from two different populations, links the clusters across the conditions and identifies marker genes for these changes. The package was designed for scRNA-Seq data but is also applicable to many other data types, just replace cells with samples and genes with variables. The package also contains functions for estimating the parameters for SparseDC as outlined in the paper. We recommend that users further select their marker genes using the magnitude of the cluster centers. 2025-04-22
r-sparsebnutils public A set of tools for representing and estimating sparse Bayesian networks from continuous and discrete data, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>. 2025-04-22
r-sparql public Use SPARQL to pose SELECT or UPDATE queries to an end-point. 2025-04-22
r-sparktex public Generate syntax for use with the sparklines package for LaTeX. 2025-04-22
r-sparkr public Provides an R Front end for 'Apache Spark' <https://spark.apache.org>. 2025-04-22
r-sparkline public Include interactive sparkline charts <http://omnipotent.net/jquery.sparkline> in all R contexts with the convenience of 'htmlwidgets'. 2025-04-22
r-spar public This package performs robust nonparametric tests for rare variants association analysis using summation of partition approaches that incorporate gene-gene and gene-environmental interactions 2025-04-22
r-spanish public Character vector to numerical translation in Euros from Spanish spelled monetary quantities. Reverse translation from integer to Spanish. Upper limit is up to the millions range. Geocoding via Cadastral web site. 2025-04-22
r-spanel public Fit the spatial panel data models: the fixed effects, random effects and between models. 2025-04-22
r-spaero public Implements methods for anticipating the emergence and eradication of infectious diseases from surveillance time series. Also provides support for computational experiments testing the performance of such methods. 2025-04-22

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