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Augmented Linear Model (ALM)

Flexible regression supporting 24+ distributions with automatic link function selection.

alm

ps.alm(
    y: Union[pl.Expr, str],
    *x: Union[pl.Expr, str],
    distribution: str = "normal",
    with_intercept: bool = True,
) -> pl.Expr

Returns: See ALM Output

Example: 

# Fit with Laplace distribution for robust estimation
df.group_by("group").agg(
    ps.alm("y", "x1", "x2", distribution="laplace").alias("model")
)

Supported Distributions

Category Distributions
Continuous normal, laplace, student_t, logistic
Positive lognormal, loglaplace, gamma, inverse_gaussian, exponential
Bounded (0,1) beta
Count poisson, negative_binomial, binomial, geometric

Distribution Selection Guide

Use Case Recommended Distribution
Standard regression normal
Robust to outliers laplace, student_t
Heavy tails student_t
Positive continuous lognormal, gamma
Right-skewed positive gamma, inverse_gaussian
Proportions/rates beta
Count data poisson, negative_binomial
Overdispersed counts negative_binomial

See Also