//localhost:1313/getting-started/index.html Purpose Onboard a new user from install to first successful DSAMbayes run. Audience New DSAMbayes users. Analysts running DSAMbayes through R scripts or CLI. Pages Page Topic Install and Setup Prerequisites, installation commands, and verification Concepts What DSAMbayes does and how Bayesian MMM works Your First BLM Model Build, fit, and interpret a single-market model using the R API Your First Hierarchical Model Multi-market model with partial pooling and CRE Quickstart (YAML Runner) Minimal end-to-end CLI run from config to output inspection FAQ Answers to common questions Hugo en-gb //localhost:1313/getting-started/install-and-setup/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:1313/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: //localhost:1313/getting-started/concepts/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:1313/getting-started/concepts/index.html What is DSAMbayes? DSAMbayes is an R package that fits Bayesian marketing mix models (MMM) using Stan. It provides a familiar lm()-style interface for specifying models, adds prior and boundary controls, and delegates estimation to Stan’s Hamiltonian Monte Carlo (HMC) sampler. The result is a full posterior distribution over model parameters — not just point estimates — enabling rigorous uncertainty quantification for media contribution and budget allocation decisions. //localhost:1313/getting-started/first-blm-model/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:1313/getting-started/first-blm-model/index.html Goal Build, fit, and interpret a single-market Bayesian linear model (BLM) using the DSAMbayes R API. 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: //localhost:1313/getting-started/first-hierarchical-model/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:1313/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. 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: //localhost:1313/getting-started/quickstart/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:1313/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. 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. //localhost:1313/getting-started/faq/index.html Mon, 01 Jan 0001 00:00:00 +0000 //localhost:1313/getting-started/faq/index.html Installation and setup How long does the first Stan compilation take? 1–3 minutes on most machines. Subsequent runs use a cached binary and start sampling immediately. If compilation seems stuck, check your C++ toolchain — see Install and Setup. Do I need to set R_LIBS_USER every time? Yes, unless you add it to your shell profile (.bashrc, .zshrc, or equivalent). The repo-local .Rlib path keeps DSAMbayes and its dependencies isolated from your system R library.