Package: vibass 1.0.2

Facundo Muñoz

vibass: Materials for Introductory Course on Bayesian Learning

Practicals, data sets, helper functions and interactive 'Shiny' apps used in the introductory course on Bayesian inference at the Valencia International Bayesian Summer School. Installing 'vibass' installs all the other packages used during the course and downloads all necessary materials for working off line.

Authors:VIBASS Team [aut, cph], Facundo Muñoz [ctb, cre], Carmen Armero [ctb], Anabel Forte [ctb], David Conesa [ctb], Mark Brewer [ctb], Virgilio Gómez-Rubio [ctb]

vibass_1.0.2.tar.gz
vibass_1.0.2.zip(r-4.7)vibass_1.0.2.zip(r-4.6)vibass_1.0.2.zip(r-4.5)
vibass_1.0.2.tgz(r-4.6-any)vibass_1.0.2.tgz(r-4.5-any)
vibass_1.0.2.tar.gz(r-4.7-any)vibass_1.0.2.tar.gz(r-4.6-any)
vibass_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
vibass/json (API)
NEWS

# Install 'vibass' in R:
install.packages('vibass', repos = c('https://vabar.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/vabar/vibass/issues

Datasets:

On CRAN:

Conda:

bayesian-inferenceteaching

6.51 score 9 stars 2 scripts 487 downloads 3 exports 81 dependencies

Last updated from:dc41b60179. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK244
source / vignettesOK337
linux-release-x86_64OK245
macos-release-arm64OK232
macos-oldrel-arm64OK186
windows-develOK227
windows-releaseOK187
windows-oldrelOK175
wasm-releaseOK201

Exports:available_appssummary_tablevibass_app

Dependencies:attemptbase64encBayesXsrcbootbslibcachemclicolorspacecommonmarkconfigcpp11digestdplyrevaluateextraDistrfarverfastmapfontawesomefsgenericsggplot2gluegolemgtableherehighrhtmltoolshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelifecyclelme4magrittrMASSMatrixmemoisemgcvmimeminqanlmenloptrotelpillarpkgconfigpromisespurrrR2BayesXR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrprojrootrstudioapiS7sassscalesshinysourcetoolsstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrxfunxtableyaml

Practical 1: Binary data

VIBASS

Rendered fromp1.Rmdusingknitr::rmarkdownon Jun 07 2026.

Last update: 2025-09-10
Started: 2021-05-31

Practical 2: Count data

VIBASS

Rendered fromp2count.Rmdusingknitr::rmarkdownon Jun 07 2026.

Last update: 2024-07-08
Started: 2023-07-02

Practical 2: Normal data

VIBASS

Rendered fromp2normal.Rmdusingknitr::rmarkdownon Jun 07 2026.

Last update: 2025-06-26
Started: 2023-07-02

Practical 3: Bayesian polynomial regression

VIBASS

Rendered fromp3.Rmdusingknitr::rmarkdownon Jun 07 2026.

Last update: 2024-07-08
Started: 2021-06-25

Practical 4: Simulation-based Bayesian inference

VIBASS

Rendered fromp4.Rmdusingknitr::rmarkdownon Jun 07 2026.

Last update: 2025-07-02
Started: 2021-07-01

Practical 5: Numerical approaches

VIBASS

Rendered fromp5.Rmdusingknitr::rmarkdownon Jun 07 2026.

Last update: 2025-07-02
Started: 2021-07-08

Practical 6: Software and GLMs

VIBASS

Rendered fromp6.Rmdusingknitr::rmarkdownon Jun 07 2026.

Last update: 2025-07-02
Started: 2021-07-08

Practical 7: Bayesian Hierarchical Modelling

VIBASS

Rendered fromp7.Rmdusingknitr::rmarkdownon Jun 07 2026.

Last update: 2025-07-10
Started: 2021-07-08

Practical 8: Optional Extra and Advanced Material

VIBASS

Rendered fromp8.Rmdusingknitr::rmarkdownon Jun 07 2026.

Last update: 2025-07-05
Started: 2021-07-08