Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
Multiple imputation of missing values: chained equations (MICE) with an analysis model pooled by Rubin’s rules and the fraction of missing information, and Bayesian multivariate-normal imputation of gaps in a panel of series with the posterior mean and covariance.
stats_imputation: 2 tools.
| Tool | What it does | Notes |
|---|---|---|
stats_mice | Multiple imputation by chained equations: every column with gaps is imputed in turn from a regression on the others (predictive mean matching), the analysis model (formula) is fitted to each imputed data set, and the fits are pooled by Rubin’s rules. | up to 300 s |
stats_bayes_gauss_imputation | Bayesian imputation under a multivariate normal model: a Gibbs sampler draws the missing values, the mean vector and the covariance matrix in turn (conjugate priors, on internally standardized columns so the default priors are weak at any scale). | up to 180 s |
fincept_describe_tool.