Showing 200 of total 1137 results (show query)

myllym

GET:Global Envelopes

Implementation of global envelopes for a set of general d-dimensional vectors T in various applications. A 100(1-alpha)% global envelope is a band bounded by two vectors such that the probability that T falls outside this envelope in any of the d points is equal to alpha. Global means that the probability is controlled simultaneously for all the d elements of the vectors. The global envelopes can be used for graphical Monte Carlo and permutation tests where the test statistic is a multivariate vector or function (e.g. goodness-of-fit testing for point patterns and random sets, functional analysis of variance, functional general linear model, n-sample test of correspondence of distribution functions), for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot) and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See Myllymäki and Mrkvička (2024) <doi:10.18637/jss.v111.i03>, Myllymäki et al. (2017) <doi:10.1111/rssb.12172>, Mrkvička and Myllymäki (2023) <doi:10.1007/s11222-023-10275-7>, Mrkvička et al. (2016) <doi:10.1016/j.spasta.2016.04.005>, Mrkvička et al. (2017) <doi:10.1007/s11222-016-9683-9>, Mrkvička et al. (2020) <doi:10.14736/kyb-2020-3-0432>, Mrkvička et al. (2021) <doi:10.1007/s11009-019-09756-y>, Myllymäki et al. (2021) <doi:10.1016/j.spasta.2020.100436>, Mrkvička et al. (2022) <doi:10.1002/sim.9236>, Dai et al. (2022) <doi:10.5772/intechopen.100124>, Dvořák and Mrkvička (2022) <doi:10.1007/s00180-021-01134-y>, Mrkvička et al. (2023) <doi:10.48550/arXiv.2309.04746>, and Konstantinou et al. (2024) <doi: 10.1007/s00180-024-01569-z>.

Maintained by Mari Myllymäki. Last updated 4 months ago.

28.3 match 11 stars 9.33 score 46 scripts 5 dependents

andysouth

rworldmap:Mapping Global Data

Enables mapping of country level and gridded user datasets.

Maintained by Andy South. Last updated 2 years ago.

18.4 match 30 stars 11.83 score 3.2k scripts 14 dependents

r-spatial

spdep:Spatial Dependence: Weighting Schemes, Statistics

A collection of functions to create spatial weights matrix objects from polygon 'contiguities', from point patterns by distance and tessellations, for summarizing these objects, and for permitting their use in spatial data analysis, including regional aggregation by minimum spanning tree; a collection of tests for spatial 'autocorrelation', including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord' (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel' general cross product statistic, Empirical Bayes estimates and 'Assunção/Reis' (1999) <doi:10.1002/(SICI)1097-0258(19990830)18:16%3C2147::AID-SIM179%3E3.0.CO;2-I> Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x> and multicoloured join count statistics, 'APLE' ('Li 'et al.' ) <doi:10.1111/j.1538-4632.2007.00708.x>, local 'Moran's I', 'Gearys C' ('Anselin' 1995) <doi:10.1111/j.1538-4632.1995.tb00338.x> and 'Getis/Ord' G ('Ord' and 'Getis' 1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>, 'saddlepoint' approximations ('Tiefelsdorf' 2002) <doi:10.1111/j.1538-4632.2002.tb01084.x> and exact tests for global and local 'Moran's I' ('Bivand et al.' 2009) <doi:10.1016/j.csda.2008.07.021> and 'LOSH' local indicators of spatial heteroscedasticity ('Ord' and 'Getis') <doi:10.1007/s00168-011-0492-y>. The implementation of most of these measures is described in 'Bivand' and 'Wong' (2018) <doi:10.1007/s11749-018-0599-x>, with further extensions in 'Bivand' (2022) <doi:10.1111/gean.12319>. 'Lagrange' multiplier tests for spatial dependence in linear models are provided ('Anselin et al'. 1996) <doi:10.1016/0166-0462(95)02111-6>, as are 'Rao' score tests for hypothesised spatial 'Durbin' models based on linear models ('Koley' and 'Bera' 2023) <doi:10.1080/17421772.2023.2256810>. A local indicators for categorical data (LICD) implementation based on 'Carrer et al.' (2021) <doi:10.1016/j.jas.2020.105306> and 'Bivand et al.' (2017) <doi:10.1016/j.spasta.2017.03.003> was added in 1.3-7. From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions previously present in this package are defunct in 'spdep' and may be found in 'spatialreg'.

Maintained by Roger Bivand. Last updated 18 days ago.

spatial-autocorrelationspatial-dependencespatial-weights

10.0 match 131 stars 16.62 score 6.0k scripts 107 dependents

ropensci

GSODR:Global Surface Summary of the Day ('GSOD') Weather Data Client

Provides automated downloading, parsing, cleaning, unit conversion and formatting of Global Surface Summary of the Day ('GSOD') weather data from the from the USA National Centers for Environmental Information ('NCEI'). Units are converted from from United States Customary System ('USCS') units to International System of Units ('SI'). Stations may be individually checked for number of missing days defined by the user, where stations with too many missing observations are omitted. Only stations with valid reported latitude and longitude values are permitted in the final data. Additional useful elements, saturation vapour pressure ('es'), actual vapour pressure ('ea') and relative humidity ('RH') are calculated from the original data using the improved August-Roche-Magnus approximation (Alduchov & Eskridge 1996) and included in the final data set. The resulting metadata include station identification information, country, state, latitude, longitude, elevation, weather observations and associated flags. For information on the 'GSOD' data from 'NCEI', please see the 'GSOD' 'readme.txt' file available from, <https://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>.

Maintained by Adam H. Sparks. Last updated 10 days ago.

us-nceimeteorological-dataglobal-weatherweatherweather-datameteorologystation-datasurface-weatherdata-accessus-ncdcdaily-datadaily-weatherglobal-datagsodhistorical-datahistorical-weatherncdcnceiweather-informationweather-stations

18.3 match 94 stars 8.70 score 116 scripts

josiahparry

sfdep:Spatial Dependence for Simple Features

An interface to 'spdep' to integrate with 'sf' objects and the 'tidyverse'.

Maintained by Dexter Locke. Last updated 6 months ago.

r-spatialspatial

19.5 match 130 stars 7.01 score 130 scripts

pik-piam

mrremind:MadRat REMIND Input Data Package

The mrremind packages contains data preprocessing for the REMIND model.

Maintained by Lavinia Baumstark. Last updated 3 days ago.

13.2 match 4 stars 6.25 score 15 scripts 1 dependents

sbgraves237

Ecdat:Data Sets for Econometrics

Data sets for econometrics, including political science.

Maintained by Spencer Graves. Last updated 4 months ago.

10.9 match 2 stars 7.25 score 740 scripts 3 dependents

dnychka

fields:Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

Maintained by Douglas Nychka. Last updated 9 months ago.

fortran

5.6 match 15 stars 12.60 score 7.7k scripts 295 dependents

yihui

xfun:Supporting Functions for Packages Maintained by 'Yihui Xie'

Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.

Maintained by Yihui Xie. Last updated 3 days ago.

3.5 match 145 stars 18.18 score 916 scripts 4.4k dependents

bioc

GlobalAncova:Global test for groups of variables via model comparisons

The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany.

Maintained by Manuela Hummel. Last updated 5 months ago.

microarrayonechanneldifferentialexpressionpathwaysregression

10.7 match 5.32 score 9 scripts 1 dependents

henrikbengtsson

R.utils:Various Programming Utilities

Utility functions useful when programming and developing R packages.

Maintained by Henrik Bengtsson. Last updated 1 years ago.

3.5 match 63 stars 13.74 score 5.7k scripts 814 dependents

bioc

BASiCS:Bayesian Analysis of Single-Cell Sequencing data

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend.

Maintained by Catalina Vallejos. Last updated 5 months ago.

immunooncologynormalizationsequencingrnaseqsoftwaregeneexpressiontranscriptomicssinglecelldifferentialexpressionbayesiancellbiologybioconductor-packagegene-expressionrcpprcpparmadilloscrna-seqsingle-cellopenblascppopenmp

4.3 match 83 stars 10.26 score 368 scripts 1 dependents

merliseclyde

BAS:Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Maintained by Merlise Clyde. Last updated 4 months ago.

bayesianbayesian-inferencegeneralized-linear-modelslinear-regressionlogistic-regressionmcmcmodel-selectionpoisson-regressionpredictive-modelingregressionvariable-selectionfortranopenblas

4.0 match 44 stars 10.81 score 420 scripts 3 dependents

mikejohnson51

climateR:climateR

Find, subset, and retrive geospatial data by AOI.

Maintained by Mike Johnson. Last updated 3 months ago.

aoiclimatedatasetgeospatialgridded-climate-dataweather

4.9 match 187 stars 8.74 score 156 scripts 1 dependents

venelin

PCMBase:Simulation and Likelihood Calculation of Phylogenetic Comparative Models

Phylogenetic comparative methods represent models of continuous trait data associated with the tips of a phylogenetic tree. Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above models can be associated with different branches of the tree. The PCMBase package is limited to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic models. The PCMFit package provides functionality for inference of these models to tree and trait data. The package web-site <https://venelin.github.io/PCMBase/> provides access to the documentation and other resources.

Maintained by Venelin Mitov. Last updated 10 months ago.

4.9 match 6 stars 7.56 score 85 scripts 3 dependents

thinkr-open

checkhelper:Deal with Check Outputs

Deal with packages 'check' outputs and reduce the risk of rejection by 'CRAN' by following policies.

Maintained by Sebastien Rochette. Last updated 1 years ago.

5.3 match 34 stars 6.74 score 18 scripts

bquast

gvc:Global Value Chains Tools

Several tools for Global Value Chain ('GVC') analysis are implemented.

Maintained by Bastiaan Quast. Last updated 3 years ago.

5.6 match 21 stars 5.67 score 45 scripts

andrewzm

FRK:Fixed Rank Kriging

A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie <doi:10.18637/jss.v108.i10> describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.

Maintained by Andrew Zammit-Mangion. Last updated 6 months ago.

cpp

3.4 match 71 stars 8.70 score 188 scripts 1 dependents

idem-lab

sdmtools:Utility tools for Species Distribution Modelling

What the package does (one paragraph).

Maintained by Gerry Ryan. Last updated 3 months ago.

4.5 match 9 stars 6.13 score 674 scripts