Showing 9 of total 9 results (show query)
mrcieu
TwoSampleMR:Two Sample MR Functions and Interface to MRC Integrative Epidemiology Unit OpenGWAS Database
A package for performing Mendelian randomization using GWAS summary data. It uses the IEU OpenGWAS database <https://gwas.mrcieu.ac.uk/> to automatically obtain data, and a wide range of methods to run the analysis.
Maintained by Gibran Hemani. Last updated 4 days ago.
476 stars 11.27 score 1.7k scripts 1 dependents
trinker
sentimentr:Calculate Text Polarity Sentiment
Calculate text polarity sentiment at the sentence level and optionally aggregate by rows or grouping variable(s).
Maintained by Tyler Rinker. Last updated 3 years ago.
amplifierpolaritysentimentsentiment-analysisvalence-shifter
435 stars 9.60 score 680 scripts 2 dependents
radiant-rstats
radiant.data:Data Menu for Radiant: Business Analytics using R and Shiny
The Radiant Data menu includes interfaces for loading, saving, viewing, visualizing, summarizing, transforming, and combining data. It also contains functionality to generate reproducible reports of the analyses conducted in the application.
Maintained by Vincent Nijs. Last updated 5 months ago.
53 stars 8.25 score 146 scripts 6 dependents
fbartos
RoBMA:Robust Bayesian Meta-Analyses
A framework for estimating ensembles of meta-analytic and meta-regression models (assuming either presence or absence of the effect, heterogeneity, publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to combine the competing meta-analytic models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual components (e.g., effect vs. no effect; Bartoš et al., 2022, <doi:10.1002/jrsm.1594>; Maier, Bartoš & Wagenmakers, 2022, <doi:10.1037/met0000405>). Users can define a wide range of prior distributions for + the effect size, heterogeneity, publication bias (including selection models and PET-PEESE), and moderator components. The package provides convenient functions for summary, visualizations, and fit diagnostics.
Maintained by František Bartoš. Last updated 2 months ago.
meta-analysismodel-averagingpublication-biasjagsopenblascpp
9 stars 6.88 score 53 scripts
ssa-statistical-team-projects
povmap:Extension to the 'emdi' Package
The R package 'povmap' supports small area estimation of means and poverty headcount rates. It adds several new features to the 'emdi' package (see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07>). These include new options for incorporating survey weights, ex-post benchmarking of estimates, two additional transformations, several new convenient functions to assist with reporting results, and a wrapper function to facilitate access from 'Stata'.
Maintained by Ifeanyi Edochie. Last updated 5 months ago.
1 stars 4.60 score 10 scripts
krajnc
densitr:Analysing Density Profiles from Resistance Drilling of Trees
Provides various tools for analysing density profiles obtained by resistance drilling. It can load individual or multiple files and trim the starting and ending part of each density profile. Tools are also provided to trim profiles manually, to remove the trend from measurements using several methods, to plot the profiles and to detect tree rings automatically. Written with a focus on forestry use of resistance drilling in standing trees.
Maintained by Luka Krajnc. Last updated 3 years ago.
2 stars 3.90 score 9 scripts
psolymos
EDMAinR:Euclidean Distance Matrix Analysis in R
A coordinate-free approach for comparing biological shapes using landmark data based on Lele and Richtsmeier (1991) <doi:10.1002/ajpa.1330860307>.
Maintained by Peter Solymos. Last updated 2 years ago.
comparing-biological-shapescoordinate-freelandmark-datamorphometricsmultivariate-statistics
3 stars 3.78 score 9 scripts
raphaelhartmann
forceplate:Processing Force-Plate Data
Process raw force-plate data (txt-files) by segmenting them into trials and, if needed, calculating (user-defined) descriptive statistics of variables for user-defined time bins (relative to trigger onsets) for each trial. When segmenting the data a baseline correction, a filter, and a data imputation can be applied if needed. Experimental data can also be processed and combined with the segmented force-plate data. This procedure is suggested by Johannsen et al. (2023) <doi:10.6084/m9.figshare.22190155> and some of the options (e.g., choice of low-pass filter) are also suggested by Winter (2009) <doi:10.1002/9780470549148>.
Maintained by Raphael Hartmann. Last updated 13 days ago.
3.30 score