/getting-started/index.html Purpose Onboard a new user from install to first successful dsambayesplus run, then point them into the workflow guidance needed for serious modelling use. Audience New dsambayesplus users. Analysts running dsambayesplus through R scripts or CLI. Recommended route Install and update the R package for R API use; use Install and Setup or Windows / RStudio setup for repository runners Concepts Your First BLM Model or Your First Hierarchical Model Quickstart (YAML Runner) FAQ Principled Bayesian Workflow If you are coming from classical econometrics, read Frequentist to Bayesian Translation before customising priors or interpreting diagnostics. Hugo en-gb /getting-started/install-and-update/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/install-and-update/index.html Use the standalone installer for colleagues who call dsambayesplus from R or RStudio. Downloading the whole repository is unnecessary for this workflow. Updates are explicit: run the installer before loading the package, when you intend to change versions. Do not put it in .Rprofile or a model-fitting script. The installer needs internet access and personal read access to the private tandpds/dsambayesplus repository. It installs source packages. R >= 4.1 and a working C++ toolchain for Stan are still required; see the Windows guide or Linux/macOS guide for toolchain setup. /getting-started/install-and-setup/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/install-and-setup/index.html Audience Engineers and analysts setting up dsambayesplus for local development or modelling runs. This page is written for a Unix shell (Linux or macOS). If you are on Windows using RStudio, especially on a work-issued machine without full administrator rights, use Install and Setup (Windows / RStudio) instead. For package API use, follow Install and update the R package. The checkout instructions below apply when you need repository runners, bundled examples or development tools. Installing the package does not require a checkout. /getting-started/install-and-setup-windows-rstudio/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/install-and-setup-windows-rstudio/index.html Audience Windows users running dsambayesplus through RStudio (or VS Code) on a work-issued machine without full administrator rights. If you use Linux or macOS, or you are comfortable with a Unix shell, use Install and Setup instead; this page only covers the parts that differ on Windows. This page assumes no prior bash experience. Every command below runs from the RStudio Console or an RStudio Terminal tab. /getting-started/concepts/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/concepts/index.html Purpose Give new dsambayesplus users a compact conceptual orientation before they move into tutorials, runner usage, or the workflow section. This page is intentionally introductory. It does not try to be the full methodology guide for Bayesian MMM. For that, use Principled Bayesian Workflow. What is dsambayesplus? dsambayesplus is an R package for Bayesian marketing mix modelling built on Stan. It provides: /getting-started/first-blm-model/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/first-blm-model/index.html Goal Build, fit, and interpret a single-market Bayesian linear model (BLM) using the dsambayesplus R API. This page is a hands-on tutorial. For the broader methodological questions behind prior-setting and diagnostics, use the workflow pages: Stage 2: Model and Priors Stage 4: Computation and Sampler Stage 5: Model Adequacy Prerequisites dsambayesplus installed locally (see Install and Setup). Familiarity with R and lm()-style formulas. Dataset This walkthrough uses the tracked synthetic dataset at data/timeseries/demo_data_synthetic.csv. It contains weekly observations for a single series with columns for: /getting-started/first-hierarchical-model/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/first-hierarchical-model/index.html Goal Build, fit, and interpret a multi-market hierarchical model with partial pooling and optional CRE (Mundlak) correction using the dsambayesplus R API. This page is a hands-on tutorial. For the broader methodological questions behind prior-setting, diagnostics, and interpretation, use the workflow pages: Stage 2: Model and Priors Stage 4: Computation and Sampler Stage 5: Model Adequacy Prerequisites dsambayesplus installed locally (see Install and Setup). Familiarity with the BLM workflow (see Your First BLM Model). Understanding of random-effects / mixed-model concepts. Dataset This walkthrough uses the tracked data/geo_panel/demo_data_geo_panel.csv , a panel dataset with weekly observations across multiple geos. Key columns: /getting-started/quickstart/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/quickstart/index.html Goal Complete one reproducible dsambayesplus runner execution from validation to artefact inspection, then load the fitted model in R to explore the results interactively. This page is operational by design. It teaches you how to run the package, not the full modelling methodology. After the quickstart succeeds, use Principled Bayesian Workflow before treating outputs as decision-ready. Before you start Complete the setup in Install and Setup. If you want to build a model interactively from R code instead of YAML, see Your First BLM Model. /getting-started/faq/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/faq/index.html This page is for short, practical answers. For the bigger methodological questions, start with: What Principled Means Frequentist to Bayesian Translation Stage 2: Model and Priors Stage 4: Computation and Sampler Stage 5: Model Adequacy Installation and setup How long does the first Stan compilation take? Usually 1 to 3 minutes. Subsequent runs typically reuse the cached binary. If compilation appears stuck, check the C++ toolchain in Install and Setup.