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bioc
Voyager:From geospatial to spatial omics
SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results.
Maintained by Lambda Moses. Last updated 3 months ago.
geneexpressionspatialtranscriptomicsvisualizationbioconductoredaesdaexploratory-data-analysisomicsspatial-statisticsspatial-transcriptomics
11.9 match 87 stars 8.71 score 173 scripts
connordonegan
geostan:Bayesian Spatial Analysis
For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.
Maintained by Connor Donegan. Last updated 3 months ago.
bayesianbayesian-inferencebayesian-statisticsepidemiologymodelingpublic-healthrspatialspatialstancpp
1.2 match 80 stars 8.82 score 46 scripts
laboratorio-de-pedometria
pedometrics:Miscellaneous Pedometric Tools
An R implementation of methods employed in the field of pedometrics, soil science discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil using statistical and computational methods. The methods found here include the calibration of linear regression models using covariate selection strategies, computation of summary validation statistics for predictions, generation of summary plots, evaluation of the local quality of a geostatistical model of uncertainty, and so on. Other functions simply extend the functionalities of or facilitate the usage of functions from other packages that are commonly used for the analysis of soil data. Formerly available versions of suggested packages no longer available from CRAN can be obtained from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/>.
Maintained by Alessandro Samuel-Rosa. Last updated 3 years ago.
1.8 match 6 stars 4.00 score 33 scripts
cran
lctools:Local Correlation, Spatial Inequalities, Geographically Weighted Regression and Other Tools
Provides researchers and educators with easy-to-learn user friendly tools for calculating key spatial statistics and to apply simple as well as advanced methods of spatial analysis in real data. These include: Local Pearson and Geographically Weighted Pearson Correlation Coefficients, Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation (Global and Local Moran's I), several Geographically Weighted Regression techniques and other Spatial Analysis tools (other geographically weighted statistics). This package also contains functions for measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.
Maintained by Stamatis Kalogirou. Last updated 12 months ago.
1.1 match 1 stars 3.03 score 53 scripts