Showing 8 of total 8 results (show query)
wviechtb
metafor:Meta-Analysis Package for R
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit equal-, fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010) <doi:10.18637/jss.v036.i03>.
Maintained by Wolfgang Viechtbauer. Last updated 2 days ago.
meta-analysismixed-effectsmultilevel-modelsmultivariate
250 stars 16.32 score 4.9k scripts 92 dependents
jgx65
hierfstat:Estimation and Tests of Hierarchical F-Statistics
Estimates hierarchical F-statistics from haploid or diploid genetic data with any numbers of levels in the hierarchy, following the algorithm of Yang (Evolution(1998), 52:950). Tests via randomisations the significance of each F and variance components, using the likelihood-ratio statistics G (Goudet et al. (1996) <https://academic.oup.com/genetics/article/144/4/1933/6017091>). Estimates genetic diversity statistics for haploid and diploid genetic datasets in various formats, including inbreeding and coancestry coefficients, and population specific F-statistics following Weir and Goudet (2017) <https://academic.oup.com/genetics/article/206/4/2085/6072590>.
Maintained by Jerome Goudet. Last updated 4 months ago.
devtoolsfstatisticsgwashierfstatkinshippopulation-geneticspopulation-genomicsquantitative-geneticssimulations
25 stars 11.06 score 560 scripts 5 dependents
lselzer
multcompView:Visualizations of Paired Comparisons
Convert a logical vector or a vector of p-values or a correlation, difference, or distance matrix into a display identifying the pairs for which the differences were not significantly different. Designed for use in conjunction with the output of functions like TukeyHSD, dist{stats}, simint, simtest, csimint, csimtest{multcomp}, friedmanmc, kruskalmc{pgirmess}.
Maintained by Luciano Selzer. Last updated 1 years ago.
9.94 score 1.8k scripts 172 dependents
gi0na
ghypernet:Fit and Simulate Generalised Hypergeometric Ensembles of Graphs
Provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG). To learn how to use it, check the vignettes for a quick tutorial. Please reference its use as Casiraghi, G., Nanumyan, V. (2019) <doi:10.5281/zenodo.2555300> together with those relevant references from the one listed below. The package is based on the research developed at the Chair of Systems Design, ETH Zurich. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <arXiv:1607.02441>. Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) <doi:10.1007/978-3-319-67256-4_11>. Casiraghi, G., (2017) <arXiv:1702.02048> Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) <doi:10.1145/3341161.3342926> Casiraghi, G. (2019) <doi:10.1007/s41109-019-0241-1>. Casiraghi, G., Nanumyan, V. (2021) <doi:10.1038/s41598-021-92519-y>. Casiraghi, G. (2021) <doi:10.1088/2632-072X/ac0493>.
Maintained by Giona Casiraghi. Last updated 12 months ago.
data-miningdata-sciencegraphsnetworknetwork-analysisrandom-graph-generationrandom-graphs
8 stars 5.68 score 20 scripts
opasche
EQRN:Extreme Quantile Regression Neural Networks for Risk Forecasting
This framework enables forecasting and extrapolating measures of conditional risk (e.g. of extreme or unprecedented events), including quantiles and exceedance probabilities, using extreme value statistics and flexible neural network architectures. It allows for capturing complex multivariate dependencies, including dependencies between observations, such as sequential dependence (time-series). The methodology was introduced in Pasche and Engelke (2024) <doi:10.1214/24-AOAS1907> (also available in preprint: Pasche and Engelke (2022) <doi:10.48550/arXiv.2208.07590>).
Maintained by Olivier C. Pasche. Last updated 9 days ago.
7 stars 4.24 score
sigbertklinke
exams.forge:Support for Compiling Examination Tasks using the 'exams' Package
The main aim is to further facilitate the creation of exercises based on the package 'exams' by Grün, B., and Zeileis, A. (2009) <doi:10.18637/jss.v029.i10>. Creating effective student exercises involves challenges such as creating appropriate data sets and ensuring access to intermediate values for accurate explanation of solutions. The functionality includes the generation of univariate and bivariate data including simple time series, functions for theoretical distributions and their approximation, statistical and mathematical calculations for tasks in basic statistics courses as well as general tasks such as string manipulation, LaTeX/HTML formatting and the editing of XML task files for 'Moodle'.
Maintained by Sigbert Klinke. Last updated 9 months ago.
2.70 score 1 scripts
cran
bingat:Binary Graph Analysis Tools
Tools to analyze binary graph objects.
Maintained by Berkley Shands. Last updated 8 years ago.
1.00 score