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epiforecasts
scoringutils:Utilities for Scoring and Assessing Predictions
Facilitate the evaluation of forecasts in a convenient framework based on data.table. It allows user to to check their forecasts and diagnose issues, to visualise forecasts and missing data, to transform data before scoring, to handle missing forecasts, to aggregate scores, and to visualise the results of the evaluation. The package mostly focuses on the evaluation of probabilistic forecasts and allows evaluating several different forecast types and input formats. Find more information about the package in the Vignettes as well as in the accompanying paper, <doi:10.48550/arXiv.2205.07090>.
Maintained by Nikos Bosse. Last updated 28 days ago.
forecast-evaluationforecasting
52 stars 11.37 score 326 scripts 7 dependents
bioc
ReactomeGSA:Client for the Reactome Analysis Service for comparative multi-omics gene set analysis
The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited.
Maintained by Johannes Griss. Last updated 4 months ago.
genesetenrichmentproteomicstranscriptomicssystemsbiologygeneexpressionreactome
22 stars 8.50 score 67 scripts 1 dependents
tylermorganwall
skpr:Design of Experiments Suite: Generate and Evaluate Optimal Designs
Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) <doi:10.18637/jss.v099.i01>.
Maintained by Tyler Morgan-Wall. Last updated 24 days ago.
design-of-experimentslinear-modelslinear-regressionmonte-carlooptimal-designspowersplit-plot-designssurvival-analysiscpp
118 stars 6.89 score 35 scripts