Package: fido 1.1.4
fido: Bayesian Multinomial Logistic Normal Regression
Provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation and Laplace Approximation as developed in Silverman et. Al. (2022) <https://www.jmlr.org/papers/v23/19-882.html>. Key functionality is implemented in C++ for scalability. 'fido' replaces the previous package 'stray'.
Authors:
fido_1.1.4.tar.gz
fido_1.1.4.zip(r-4.7)fido_1.1.4.zip(r-4.6)fido_1.1.4.zip(r-4.5)
fido_1.1.4.tgz(r-4.6-x86_64)fido_1.1.4.tgz(r-4.6-arm64)fido_1.1.4.tgz(r-4.5-x86_64)fido_1.1.4.tgz(r-4.5-arm64)
fido_1.1.4.tar.gz(r-4.7-arm64)fido_1.1.4.tar.gz(r-4.7-x86_64)fido_1.1.4.tar.gz(r-4.6-arm64)fido_1.1.4.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
card.svg |card.png
fido/json (API)
NEWS
| # Install 'fido' in R: |
| install.packages('fido', repos = c('https://jsilve24.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jsilve24/fido/issues
Pkgdown/docs site:https://jsilve24.github.io
- mallard - Data from Silverman et al. (2018) Microbiome
- mallard_family - Data from Silverman et al. (2018) Microbiome
- metadata - Data from Silverman et al. (2019) bioRxiv
- RISK_CCFA_otu - Data from Gevers et al.
- RISK_CCFA_sam - Data from Gevers et al.
- RISK_CCFA_tax - Data from Gevers et al.
- Y - Data from Silverman et al. (2019) bioRxiv
Last updated from:36e97a883d. Checks:12 OK, 1 FAIL. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 383 | ||
| linux-devel-x86_64 | OK | 393 | ||
| source / vignettes | OK | 549 | ||
| linux-release-arm64 | OK | 421 | ||
| linux-release-x86_64 | OK | 399 | ||
| macos-release-arm64 | OK | 262 | ||
| macos-release-x86_64 | OK | 746 | ||
| macos-oldrel-arm64 | OK | 233 | ||
| macos-oldrel-x86_64 | OK | 454 | ||
| windows-devel | OK | 458 | ||
| windows-release | OK | 470 | ||
| windows-oldrel | OK | 474 | ||
| wasm-release | FAIL | 172 |
Exports:alralr_arrayalrInvalrInv_arraybassetcheck_dimsclr_arrayconjugateLinearModelcreate_default_ilr_basegather_arraygradPibbleCollapsedhessPibbleCollapsedlambda_to_iqlrLINEARloglikPibbleCollapsedminiclominiclo_arraynamenames_categoriesnames_categories<-names_coordsnames_covariatesnames_covariates<-names_samplesnames_samples<-ncategoriesncovariatesniternsamplesoalroalrInvoalrvar2alrvaroalrvar2clrvaroalrvar2ilrvaroclroclrInvoclrvar2alrvaroclrvar2ilrvaroglroglrInvoilroilrInvoilrvar2alrvaroilrvar2clrvaroilrvar2ilrvaroptimPibbleCollapsedorthusorthus_simorthus_tidy_samplesorthusfitpibblepibble_simpibble_tidy_samplespibblefitppcppc_summaryr2random_pibble_initreapply_coordrefitreqsample_priorSEstore_coordsummarise_posteriorto_alrto_clrto_ilrto_proportionsuncollapsePibbleuncollapsePibble_sigmaKnownverify
Dependencies:abindarrayhelpersbackportsBHcheckmateclicodacpp11distributionaldplyrfarvergenericsggdistggplot2gluegtableisobandlabelinglatticelifecyclemagrittrmatrixStatsnumDerivpillarpkgconfigposteriorpurrrquadprogR6RColorBrewerRcppRcppEigenRcppGSLRcppNumericalRcppZigguratrlangS7scalesstringistringrsvUnittensorAtibbletidybayestidyrtidyselectutf8vctrsviridisLitewithr
Example of using Fido for measuring and mitigating PCR Bias
Rendered frommitigating-pcrbias.Rmdusingknitr::rmarkdownon May 22 2026.Last update: 2023-05-30
Started: 2020-09-22
Introduction to fido::Pibble
Rendered fromintroduction-to-fido.Rmdusingknitr::rmarkdownon May 22 2026.Last update: 2024-05-20
Started: 2020-08-06
Joint Modeling (e.g., Multiomics) with fido::Orthus
Rendered fromorthus.Rmdusingknitr::rmarkdownon May 22 2026.Last update: 2024-05-03
Started: 2019-08-06
Non-linear models with fido::basset
Rendered fromnon-linear-models.Rmdusingknitr::rmarkdownon May 22 2026.Last update: 2024-05-14
Started: 2019-04-25
Picking Priors
Rendered frompicking_priors.Rmdusingknitr::rmarkdownon May 22 2026.Last update: 2023-06-14
Started: 2019-06-22
