Install RegiStream

Stata

Requires Stata 16.0 or later.

Install

Each package installs separately. registream is the shared core (configuration, first-run wizard, update management, shared utilities); modules require it.

net install registream, from("https://registream.org/install/stata/registream/latest") replace
net install autolabel,  from("https://registream.org/install/stata/autolabel/latest")  replace
net install datamirror, from("https://registream.org/install/stata/datamirror/latest") replace

Install registream first. Modules check for it at runtime and print install instructions if missing. You can skip any module you don't need.

Python

Requires Python 3.11 or later.

From PyPI

pip install registream

The registream meta-package installs the full stack (core plus autolabel). For the shared core only, use pip install registream-core.

API surface: df.rs.autolabel(), scope(), suggest(), df.rs.lookup(), info(), cite(), with pandas accessor + shortcut monkey-patches and matplotlib/seaborn label integration. See the Python reference for full signatures.

R

Requires R 4.1 or later.

Direct install (current)

install.packages(
  c("registream", "autolabel"),
  repos = c("https://registream.org/r/",
            "https://cloud.r-project.org/"),
  type = "source"
)

The second repos entry is the CRAN fallback for transitive deps (curl, digest, jsonlite, haven). Source install only, no compiled code.

API surface: autolabel(), scope(), suggest(), rs_lookup(), rs_lab(), info(), cite(). See the R reference for full signatures.

First run

The first time you call an autolabel command, a short setup wizard picks a mode:

• Offline — no network, no telemetry, no auto-update checks. For air-gapped environments (MONA, Forskermaskinen, Dapla).
• Standard — network allowed (update checks), no telemetry. Recommended for most users.
• Full — network allowed, batched usage telemetry on to help improve RegiStream.

On R only, a second prompt asks where to write config + cache — ~/.registream/ (shared with Stata and Python) or R's isolated tools::R_user_dir("registream","cache"). The extra prompt is a CRAN-compliance requirement; Stata and Python always use ~/.registream/ by default (overridable by the REGISTREAM_DIR env var on Python, or the registream_dir global on Stata). You only see the wizard once.

use my_register_data.dta, clear
autolabel variables, domain(scb) lang(eng)
* → setup wizard runs once, then labels are applied

Secure environments

Register-data research often happens in secure environments (MONA for SCB, Forskermaskinen for DST, Dapla for SSB, and others). These have no internet access, so autolabel update datasets can't reach registream.org. The workflow is: download on a dev machine → transfer in via the agency's file-in channel → install offline.

Full walkthrough (with per-environment notes for MONA, Forskermaskinen, and Dapla): Secure-environment install →

Institutional setup

Deploying RegiStream to a research group, a department, or a secure-env project tenant? Shared configuration + private domains let you run autolabel variables, domain(hospital) on in-house register data with no internet and no coordination with registream.org.

Full walkthrough (shared config, private-domain construction, hybrid deployments): Institutional setup →

Verify install

* Stata
registream version
registream info

# Python
python -m registream version
python -m registream.autolabel version

# R
library(autolabel); info()

Each info command prints the installed version, the config and cache directory, and the current settings (internet access, telemetry, auto-update). On Stata, registream version and registream info report the core version; module versions come from autolabel and the per-module commands.