sqlm 1.1.0

Added warnings parameter to lm_sql() to control warning verbosity:
- "full" (default): Detailed warnings with diagnostic tips
- "minimal": One-line abbreviated warnings
- "none": Suppress all warnings
diagnose_lm_sql() now detects near-perfect collinearity between numeric predictors (correlation >= 0.999).
diagnose_lm_sql() now supports dot formulas (y ~ .).

All user-facing output now uses the cli package for consistent, styled formatting with colors and icons.
print.lm_sql_result() and print.summary_lm_sql_result() now use cli for cleaner output.
Progress bar shown during grouped regression when fitting multiple models.
Error messages use cli_abort() for better formatting and call tracking.

sqlm 1.0.0

lm_sql() fits OLS linear regression models entirely in-database via a single SQL aggregation query.
Automatic dummy encoding for character and factor predictors with CASE WHEN SQL generation.
Interaction terms (*, :) fully supported, including interactions between numeric and categorical variables.
Dot expansion (y ~ .) expands to all non-response columns.
Formula transforms: I(), log(), and sqrt() translated to SQL equivalents (POWER, LN, SQRT).
Date and datetime predictor support: Date and POSIXct columns automatically converted to numeric in SQL, matching base R’s lm() behavior.
Grouped regression via dplyr::group_by() computes multiple models in a single GROUP BY query.
No-intercept models (y ~ 0 + x) handled correctly.
S7-based result objects with broom::tidy() and broom::glance() methods.
orbital integration for in-database predictions.
Cholesky decomposition for matrix inversion with MASS::ginv() fallback for rank-deficient designs.