Install and initialize ValidMind for R

Use the ValidMind R package to document and validate models built in R. The package interfaces with the ValidMind Library through reticulate, giving you access to the full Python API from R.

Prerequisites

• You are logged in to ValidMind.
• The model you want to connect to is registered in the model inventory.¹
• The model document has a template applied.²
• You are the model owner, model validator, or another model stakeholder with write access to the model's documents.³

¹ Register models in the inventory

² Working with templates

³ Manage model stakeholders

Install R

Download and install R from CRAN:

macOS

Install via Homebrew:

brew install r

Or download the .pkg installer from CRAN.

Windows

Download and run the installer from CRAN.

Linux

On Debian/Ubuntu:

sudo apt update
sudo apt install r-base

On Fedora/RHEL:

sudo dnf install R

Install Python dependencies

Install the ValidMind Library and rpy2 so Python can interface with R models:

pip install validmind rpy2

On macOS, if rpy2 fails to find R libraries, rebuild it from source against your installed R:

R_HOME=$(Rscript -e 'cat(R.home())') pip install --no-binary :all: --force-reinstall rpy2

Install R packages

Open R and install the required packages:

install.packages(c("reticulate", "dplyr", "caTools", "knitr", "glue", "plotly", "htmltools", "rmarkdown", "DT", "base64enc"))

Install the ValidMind R package

From CRAN

install.packages("validmind")

From GitHub

devtools::install_github("validmind/validmind-library", subdir="r/validmind")

From source

Clone the repository and run from the r/validmind directory:

devtools::install()

Initialize ValidMind

Connect to your ValidMind profile by providing your credentials. Point python_version to your Python binary — for example, the one in your project's .venv:

vm_r <- vm(
  api_key = "YOUR_API_KEY",
  api_secret = "YOUR_API_SECRET",
  model = "YOUR_MODEL_ID",
  python_version = "path/to/python",
  api_host = "https://API_HOST.validmind.ai/api/v1/tracking",
  document = "documentation"
)

The document parameter specifies which document type to associate with the session:

• "documentation" — For model development
• "validation-report" — For model validation

Key APIs

Since the R package returns the full Python validmind module, you can call any Python API directly via reticulate:

# Preview the documentation template
vm_r$preview_template()

# Initialize datasets
vm_dataset <- vm_r$init_dataset(
  dataset = df,
  input_id = "my_dataset",
  target_column = "target"
)

# Initialize R models
model_path <- save_model(model)
vm_model <- vm_r$init_r_model(model_path = model_path, input_id = "model")

# Assign predictions
vm_dataset$assign_predictions(model = vm_model)

# Run the full documentation test suite
vm_r$run_documentation_tests(config = test_config)

# Run individual tests
vm_r$tests$run_test(
  "validmind.data_validation.ClassImbalance",
  inputs = list(dataset = vm_dataset)
)$log()

# List available tests
vm_r$tests$list_tests(tags = list("data_quality"), task = "classification")
vm_r$tests$list_tasks_and_tags()

Troubleshooting

libc++ not found on macOS

When calling vm() you may see an error like:

OSError: dlopen(...libllvmlite.dylib...): Library not loaded: @rpath/libc++.1.dylib

This typically means the libc++ library is installed but R cannot find it. Create a symlink:

# Find the path to libc++.1.dylib
sudo find / -name "libc++.1.dylib" 2>/dev/null

# Create a symlink (adjust the source path based on the find result)
sudo ln -s /opt/homebrew/Cellar/llvm/.../libc++.1.dylib /Library/Frameworks/R.framework/Resources/lib/libc++.1.dylib

Numba circular import error

If you see:

ImportError: cannot import name 'NumbaTypeError' from partially initialized module 'numba.core.errors'

Reinstall Numba and restart the R session:

pip install -U numba

What's next