/getting-started/index.html Purpose Onboard a new user from install to first successful DSAMbayes run, then point them into the workflow guidance needed for serious modelling use. Audience New DSAMbayes users. Analysts running DSAMbayes through R scripts or CLI. Recommended route Install and Setup Quickstart (YAML Runner) Principled Bayesian Workflow Your First BLM Model or Your First Hierarchical Model 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-setup/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/install-and-setup/index.html Audience Engineers and analysts setting up DSAMbayes for local development or modelling runs. Prerequisites R >= 4.1 — check with R --version. A C++ toolchain for Stan compilation. This is the most common source of setup issues: macOS: install Xcode Command Line Tools (xcode-select --install). Windows: install Rtools matching your R version. Ensure make is on your PATH. Linux (Ubuntu/Debian): sudo apt install build-essential. See the RStan Getting Started Guide for detailed platform instructions. A local checkout of this repository. Quick setup (recommended) Open a terminal in the repository root and run: /getting-started/concepts/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/concepts/index.html Purpose Give new DSAMbayes 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 DSAMbayes? DSAMbayes 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 DSAMbayes 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 DSAMbayes installed locally (see Install and Setup). Familiarity with R and lm()-style formulas. Dataset This walkthrough uses the synthetic dataset shipped at data/synthetic_dsam_example_wide_data.csv. It contains weekly observations for a single market 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 DSAMbayes 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 DSAMbayes 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 data/synthetic_dsam_example_hierarchical_data.csv — a panel dataset with weekly observations across multiple markets. Key columns: /getting-started/quickstart/index.html Mon, 01 Jan 0001 00:00:00 +0000 /getting-started/quickstart/index.html Goal Complete one reproducible DSAMbayes 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.