This package provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.

This package provides some basic linear algebra functionality for sparse matrices. It includes Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products.

This package performs score test using saddlepoint approximation to estimate the null distribution. It also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results.

The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".

This package provides a spatial covariate-augmented overdispersed Poisson factor model is proposed to perform efficient latent representation learning method for high-dimensional large-scale spatial count data with additional covariates.

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.

Create scaled ggplot representations of playing surfaces. Playing surfaces are drawn pursuant to rule-book specifications. This package should be used as a baseline plot for displaying any type of tracking data.

Currently there are many functions in S-PLUS that are missing in R. To facilitate the conversion of S-PLUS packages to R packages, this package provides some missing S-PLUS functionality in R.

Fast SVMlight reader and writer. SVMlight is most commonly used format for storing sparse matrices (possibly with some target variable) on disk. For additional information about SVMlight format see <http://svmlight.joachims.org/>.

Simple utilities to design and generate density functions on bounded regions in space and space-time, and simulate independent, identically distributed data therefrom. See Davies & Lawson (2019) <doi:10.1080/00949655.2019.1575066> for example.

The systemPipeShiny (SPS) framework comes with many useful utility functions. However, installing the whole framework is heavy and takes some time. If you like only a few useful utility functions from SPS, install this package is enough.

This package provides a client for running SPARQL queries directly from R. SPARQL (short for SPARQL Protocol and RDF Query Language) is a query language used to retrieve and manipulate data stored in RDF (Resource Description Framework) format.

This package provides a sparklyr extension package providing an integration with Google BigQuery'. It supports direct import/export where records are directly streamed from/to BigQuery'. In addition, data may be imported/exported via intermediate data extracts on Google Cloud Storage'.

Fits univariate and multivariate spatio-temporal random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley and Banerjee <doi:10.1016/j.envsoft.2019.104608>.

This package implements the spatially aware library size normalisation algorithm, SpaNorm. SpaNorm normalises out library size effects while retaining biology through the modelling of smooth functions for each effect. Normalisation is performed in a gene- and cell-/spot- specific manner, yielding library size adjusted data.

Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D (Wang and Huang, 2018 <doi:10.1002/env.2481>).

It can be useful to temporarily hide some text or other HTML elements in Shiny applications. Building on Spoiler-Alert.js', it is possible to select the elements to hide at startup, to partially reveal them by hovering them, and to completely show them when clicking on them.

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.

Fits, spatially predicts and temporally forecasts large amounts of space-time data using [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. Bakar and Sahu (2015) <doi:10.18637/jss.v063.i15>.

This package provides a unified interface to a variety of GSEA techniques from different bioconductor packages. Results are harmonized into a single object and can be interrogated uniformly for quick exploration and interpretation of results. Interactive exploration of GSEA results is enabled through a shiny app provided by a sparrow.shiny sibling package.

The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint.

This package provides analysis of high-dimensional single-cell splicing data. Offers a framework to extract and work with ratio-based data structures derived from single-cell RNA sequencing experiments. Provides both a modern R6 object-oriented interface and direct matrix manipulation functions. Core functionalities are implemented in C++ via Rcpp to ensure high performance and scalability on large datasets.

This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression.

SpNeigh provides methods for neighborhood-aware analysis of spatial transcriptomics data. It supports boundary detection, spatial weighting (centroid- and boundary-based), spatially informed differential expression using spline-based models, and spatial enrichment analysis via the Spatial Enrichment Index (SEI). Designed for compatibility with Seurat objects, SpatialExperiment objects and spatial data frames, SpNeigh enables interpretable, publication-ready analysis of spatial gene expression patterns.