R-universe search: wakefield

Showing 17 of total 17 results (show query)

trinker

wakefield:Generate Random Data Sets

Generates random data sets including: data.frames, lists, and vectors.

Maintained by Tyler Rinker. Last updated 5 years ago.

data-generation wakefield

66.0 match 256 stars 7.13 score 209 scripts

grealesm

RapidoPGS:A Fast and Light Package to Compute Polygenic Risk Scores

Quickly computes polygenic scores from GWAS summary statistics of either case-control or quantitative traits without parameter tuning. Reales,G., Vigorito, E., Kelemen,M., Wallace,C. (2021) <doi:10.1101/2020.07.24.220392> "RápidoPGS: A rapid polygenic score calculator for summary GWAS data without a test dataset".

Maintained by Guillermo Reales. Last updated 25 days ago.

4.9 match 13 stars 5.59 score 9 scripts

richardli

SUMMER:Small-Area-Estimation Unit/Area Models and Methods for Estimation in R

Provides methods for spatial and spatio-temporal smoothing of demographic and health indicators using survey data, with particular focus on estimating and projecting under-five mortality rates, described in Mercer et al. (2015) <doi:10.1214/15-AOAS872>, Li et al. (2019) <doi:10.1371/journal.pone.0210645>, Wu et al. (DHS Spatial Analysis Reports No. 21, 2021), and Li et al. (2023) <doi:10.48550/arXiv.2007.05117>.

Maintained by Zehang R Li. Last updated 2 months ago.

bayesian-inference small-area-estimation space-time

1.5 match 23 stars 10.28 score 134 scripts 2 dependents

jongheepark

MCMCpack:Markov Chain Monte Carlo (MCMC) Package

Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.

Maintained by Jong Hee Park. Last updated 7 months ago.

cpp

1.7 match 13 stars 9.40 score 2.6k scripts 150 dependents

richardli

surveyPrev:Mapping the Prevalence of Binary Indicators using Survey Data in Small Areas

Provides a pipeline to perform small area estimation and prevalence mapping of binary indicators using health and demographic survey data, described in Fuglstad et al. (2022) <doi:10.48550/arXiv.2110.09576> and Wakefield et al. (2020) <doi:10.1111/insr.12400>.

Maintained by Qianyu Dong. Last updated 4 days ago.

2.1 match 1 stars 5.76 score 11 scripts

rudeboybert

SpatialEpi:Methods and Data for Spatial Epidemiology

Methods and data for cluster detection and disease mapping.

Maintained by Albert Y. Kim. Last updated 2 years ago.

cpp

1.5 match 31 stars 6.68 score 146 scripts

bioc

INTACT:Integrate TWAS and Colocalization Analysis for Gene Set Enrichment Analysis

This package integrates colocalization probabilities from colocalization analysis with transcriptome-wide association study (TWAS) scan summary statistics to implicate genes that may be biologically relevant to a complex trait. The probabilistic framework implemented in this package constrains the TWAS scan z-score-based likelihood using a gene-level colocalization probability. Given gene set annotations, this package can estimate gene set enrichment using posterior probabilities from the TWAS-colocalization integration step.

Maintained by Jeffrey Okamoto. Last updated 5 months ago.

bayesian genesetenrichment

1.7 match 15 stars 5.47 score 13 scripts

md-anderson-bioinformatics

NGCHM:Next Generation Clustered Heat Maps

Next-Generation Clustered Heat Maps (NG-CHMs) allow for dynamic exploration of heat map data in a web browser. 'NGCHM' allows users to create both stand-alone HTML files containing a Next-Generation Clustered Heat Map, and .ngchm files to view in the NG-CHM viewer. See Ryan MC, Stucky M, et al (2020) <doi:10.12688/f1000research.20590.2> for more details.

Maintained by Mary A Rohrdanz. Last updated 8 days ago.

heatmap nci-itcr ng-chm

1.6 match 9 stars 5.48 score 28 scripts

md-anderson-bioinformatics

tsvio:Simple Utilities for Tab-Separated-Value (TSV) Files

Utilities for rapidly loading specified rows and/or columns of data from large tab-separated value (tsv) files (large: e.g. 1 GB file of 10000 x 10000 matrix). 'tsvio' is an R wrapper to 'C' code that creates an index file for the rows of the tsv file, and uses that index file to collect rows and/or columns from the tsv file without reading the whole file into memory.

Maintained by Mary A Rohrdanz. Last updated 1 years ago.

1.6 match 1 stars 4.18 score 5 scripts 1 dependents

md-anderson-bioinformatics

RPPASPACE:Reverse-Phase Protein Array Super Position and Concentration Evaluation

Provides tools for the analysis of reverse-phase protein arrays (RPPAs), which are also known as 'tissue lysate arrays' or simply 'lysate arrays'. The package's primary purpose is to input a set of quantification files representing dilution series of samples and control points taken from scanned RPPA slides and determine a relative log concentration value for each valid dilution series present in each slide and provide graphical visualization of the input and output data and their relationships. Other optional features include generation of quality control scores for judging the quality of the input data, spatial adjustment of sample points based on controls added to the slides, and various types of normalization of calculated values across a set of slides. The package was derived from a previous package named SuperCurve. For a detailed description of data inputs and outputs, usage information, and a list of related papers describing methods used in the package please review the vignette 'Guide_to_RPPASPACE'. 'RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data'. Bioinformatics Nov 15;38(22):5131-5133. <doi: 10.1093/bioinformatics/btac665>.

Maintained by James M. Melott. Last updated 9 months ago.

1.5 match 2 stars 4.00 score

md-anderson-bioinformatics

NGCHMSupportFiles:Support Files for Building Next Generation Clustered Heat Maps (NG-CHMs)

Support files required by the 'NGCHM' package to export Next-Generation Clustered Heat Maps (NG-CHMs) to .ngchm, HTML, and PDF files. These support files are updated mostly independently from the 'NGCHM' package, and are therefore provided separately.

Maintained by Mary A Rohrdanz. Last updated 1 months ago.

1.5 match 2.73 score 18 scripts

paulnorthrop

rust:Ratio-of-Uniforms Simulation with Transformation

Uses the generalized ratio-of-uniforms (RU) method to simulate from univariate and (low-dimensional) multivariate continuous distributions. The user specifies the log-density, up to an additive constant. The RU algorithm is applied after relocation of mode of the density to zero, and the user can choose a tuning parameter r. For details see Wakefield, Gelfand and Smith (1991) <DOI:10.1007/BF01889987>, Efficient generation of random variates via the ratio-of-uniforms method, Statistics and Computing (1991) 1, 129-133. A Box-Cox variable transformation can be used to make the input density suitable for the RU method and to improve efficiency. In the multivariate case rotation of axes can also be used to improve efficiency. From version 1.2.0 the 'Rcpp' package <https://cran.r-project.org/package=Rcpp> can be used to improve efficiency.

Maintained by Paul J. Northrop. Last updated 7 months ago.

1977 bayesian-inference kinderman monahan of posterior-samples ratio ratio-of-uniforms ratio-of-uniforms-method rcpp simulation transformation uniforms openblas cpp

0.5 match 7.13 score 36 scripts 7 dependents

md-anderson-bioinformatics

NGCHMDemoData:Demo Data for the NGCHM R Package

Package of demo data for NGCHM vignettes.

Maintained by Mary A Rohrdanz. Last updated 9 months ago.

1.6 match 2.20 score 16 scripts

lcgodoy

smile:Spatial Misalignment: Interpolation, Linkage, and Estimation

Provides functions to estimate, predict and interpolate areal data. For estimation and prediction we assume areal data is an average of an underlying continuous spatial process as in Moraga et al. (2017) <doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020) <doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020) <doi:10.1093/biostatistics/kxy041>. The interpolation methodology is (mostly) based on Goodchild and Lam (1980, ISSN:01652273).

Maintained by Lucas da Cunha Godoy. Last updated 4 months ago.

cpp

0.5 match 9 stars 6.37 score 21 scripts

paulnorthrop

bang:Bayesian Analysis, No Gibbs

Provides functions for the Bayesian analysis of some simple commonly-used models, without using Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling. The 'rust' package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution, using the generalized ratio-of-uniforms method. See Wakefield, Gelfand and Smith (1991) <DOI:10.1007/BF01889987> for details. At the moment three conjugate hierarchical models are available: beta-binomial, gamma-Poisson and a 1-way analysis of variance (ANOVA).

Maintained by Paul J. Northrop. Last updated 1 months ago.

anova bayesian beta binomial gamma gibbs hierarchical poisson

0.5 match 3 stars 5.62 score 35 scripts

annahutch

corrcoverage:Correcting the Coverage of Credible Sets from Bayesian Genetic Fine Mapping

Using a computationally efficient method, the package can be used to find the corrected coverage estimate of a credible set of putative causal variants from Bayesian genetic fine-mapping. The package can also be used to obtain a corrected credible set if required; that is, the smallest set of variants required such that the corrected coverage estimate of the resultant credible set is within some user defined accuracy of the desired coverage. Maller et al. (2012) <doi:10.1038/ng.2435>, Wakefield (2009) <doi:10.1002/gepi.20359>, Fortune and Wallace (2018) <doi:10.1093/bioinformatics/bty898>.

Maintained by Anna Hutchinson. Last updated 3 years ago.

openblas cpp

0.5 match 6 stars 3.68 score 16 scripts

ikiii

saeeb:Small Area Estimation for Count Data

Provides small area estimation for count data type and gives option whether to use covariates in the estimation or not. By implementing Empirical Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean squared error (MSE) estimators for each area. The EB estimators without covariates are obtained using the model proposed by Clayton & Kaldor (1987) <doi:10.2307/2532003>, the EB estimators with covariates are obtained using the model proposed by Wakefield (2006) <doi:10.1093/biostatistics/kxl008> and the MSE estimators are obtained using Jackknife method by Jiang et. al. (2002) <doi:10.1214/aos/1043351257>.

Maintained by Rizki Ananda Fauziah. Last updated 5 years ago.

0.5 match 1.00 score