Helper function `normal_known_variance_inputs` missing `var` parameter

[williambdean]

Summary

The normal_known_variance_inputs helper function doesn't include the required var parameter that the normal_known_variance model expects.

Current Behavior

from conjugate.helpers import normal_known_variance_inputs

data = [2.3, 1.9, 2.7, 2.1, 2.5]
inputs = normal_known_variance_inputs(data)
# Returns: {'x_total': 11.5, 'n': 5}

Expected Behavior

The helper should optionally accept and return the var parameter:

inputs = normal_known_variance_inputs(data, var=0.5)
# Returns: {'x_total': 11.5, 'n': 5, 'var': 0.5}

Problem

The model signature requires var:

def normal_known_variance(*, x_total, n, var, prior):

Currently users must do:

posterior = normal_known_variance(**inputs, var=0.5, prior=prior)

This breaks the pattern shown in documentation where helpers provide all required inputs.

Suggested Fix

def normal_known_variance_inputs(x, *, var=None, sum_fn=None, len_fn=None):
    result = {
        "x_total": sum_fn(x),
        "n": len_fn(x),
    }
    if var is not None:
        result["var"] = var
    return result

Also Affects

• normal_known_precision_inputs - missing precision parameter

Related

• Helper function normal_known_mean_inputs missing mu parameter #286 - Similar issue with normal_known_mean_inputs