The statistical model | Examples on how to use the package | Generate synthetic data | Parameter estimation with the EM algorithm | Scoring the individuals | Credible intervals for model parameters | Prevalence estimation | Predicting the latent states | Predicting scores on new data and classify them
Package: BinaryReplicates 1.0.0
BinaryReplicates: Dealing with Binary Replicates
Statistical methods for analyzing binary replicates, which are noisy binary measurements of latent binary states. Provides scoring functions (average, median, likelihood-based, and Bayesian) to estimate the probability that an individual is in the positive state. Includes maximum a posteriori estimation via the EM algorithm and full Bayesian inference via Stan. Supports classification with inconclusive decisions and prevalence estimation.
Authors:
BinaryReplicates_1.0.0.tar.gz
BinaryReplicates_1.0.0.zip(r-4.7)BinaryReplicates_1.0.0.zip(r-4.6)BinaryReplicates_1.0.0.zip(r-4.5)
BinaryReplicates_1.0.0.tgz(r-4.6-x86_64)BinaryReplicates_1.0.0.tgz(r-4.6-arm64)BinaryReplicates_1.0.0.tgz(r-4.5-x86_64)BinaryReplicates_1.0.0.tgz(r-4.5-arm64)
BinaryReplicates_1.0.0.tar.gz(r-4.7-arm64)BinaryReplicates_1.0.0.tar.gz(r-4.7-x86_64)BinaryReplicates_1.0.0.tar.gz(r-4.6-arm64)BinaryReplicates_1.0.0.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
BinaryReplicates/json (API)
| # Install 'BinaryReplicates' in R: |
| install.packages('BinaryReplicates', repos = c('https://pierrepudlo.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/pierrepudlo/binaryreplicates/issues
Datasets:
- mammography - A mammography dataset
- observed - A mammography dataset
- periodontal - A periodontal dataset
Last updated from:3bc5776cd2. Checks:12 OK, 1 FAIL. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 270 | ||
| linux-devel-x86_64 | OK | 289 | ||
| source / vignettes | OK | 325 | ||
| linux-release-arm64 | OK | 282 | ||
| linux-release-x86_64 | OK | 281 | ||
| macos-release-arm64 | OK | 244 | ||
| macos-release-x86_64 | OK | 565 | ||
| macos-oldrel-arm64 | OK | 397 | ||
| macos-oldrel-x86_64 | OK | 595 | ||
| windows-devel | OK | 293 | ||
| windows-release | OK | 278 | ||
| windows-oldrel | OK | 273 | ||
| wasm-release | FAIL | 313 |
Exports:average_scoringbayesian_prevalence_estimatebayesian_scoringBayesianFitclassify_with_scoresclassify_with_scores_cvEMcredintcvEMEMFitlikelihood_scoringMAP_scoringmedian_scoringpredict_scoresprevalence_estimate
Dependencies:abindbackportsBHcallrcheckmateclicpp11descdistributionaldplyrfarvergenericsggplot2gluegridExtragtableinlineisobandlabelinglifecycleloomagrittrmatrixStatsnumDerivotelpillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelrlangrstanrstantoolsS7scalesStanHeaderstensorAtibbletidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bayesian computations | bayesian_computations bayesian_prevalence_estimate bayesian_scoring credint |
| Fit the Bayesian model for Binary Replicates | BayesianFit |
| Classification based on a thresholding of the scores | classify_with_scores |
| Perform classification on the scores for each fold of a cvEM object | classify_with_scores_cvEM |
| Cross-validation for the EM algorithm | cvEM |
| Compute the _Maximum-A-Posteriori_ estimate with the EM algorithm | EMFit |
| A mammography dataset | mammography mammography-datasets observed |
| Non-Bayesian scoring methods | average_scoring likelihood_scoring MAP_scoring median_scoring non_bayesian_scoring |
| A periodontal dataset | periodontal |
| Compute predictive Bayesian scores | predict_scores |
| Compute the average-/median- or MAP-based prevalence estimates based on the scores | prevalence_estimate |
