Fast Generalized Linear Models with a Rust backend - statsmodels compatible

These details have been verified by PyPI

Project links

GitHub Statistics

Maintainers

PricingFrontier

These details have not been verified by PyPI

Project description

RustyStats 🦀📊

High-performance Generalized Linear Models with a Rust backend and Python API

Codebase Documentation: pricingfrontier.github.io/rustystats/

Features

Dict-First API - Programmatic model building ideal for automated workflows and agents
Fast - Parallel Rust backend for high-throughput fitting
Memory Efficient - Low memory footprint at scale
Stable - Step-halving IRLS, warm starts for robust convergence
Splines - B-splines and natural splines with auto-tuned smoothing and monotonicity
Target Encoding - Ordered target encoding for high-cardinality categoricals
Regularisation - Ridge, Lasso, and Elastic Net via coordinate descent
Lasso Credibility - Shrink toward a prior model instead of zero (CAS Monograph 13)
Validation - Design matrix checks with fix suggestions before fitting
Complete - 8 families, robust SEs, full diagnostics, VIF, partial dependence
Minimal - Only numpy and polars required

Installation

uv add rustystats

Quick Start

import rustystats as rs
import polars as pl

# Load data
data = pl.read_parquet("insurance.parquet")

# Fit a Poisson GLM for claim frequency
result = rs.glm_dict(
    response="ClaimCount",
    terms={
        "VehAge": {"type": "linear"},
        "VehPower": {"type": "linear"},
        "Area": {"type": "categorical"},
        "Region": {"type": "categorical"},
    },
    data=data,
    family="poisson",
    offset="Exposure",
).fit()

# View results
print(result.summary())

Families & Links

Family	Default Link	Use Case
`gaussian`	identity	Linear regression
`poisson`	log	Claim frequency
`binomial`	logit	Binary outcomes
`gamma`	log	Claim severity
`tweedie`	log	Pure premium (var_power=1.5)
`quasipoisson`	log	Overdispersed counts
`quasibinomial`	logit	Overdispersed binary
`negbinomial`	log	Overdispersed counts (proper distribution)

Dict-Based API

API built for programmatic model building.

result = rs.glm_dict(
    response="ClaimCount",
    terms={
        "VehAge": {"type": "bs", "monotonicity": "increasing"},  # Monotonic (auto-tuned)
        "DrivAge": {"type": "bs"},                               # Penalized smooth (default)
        "Income": {"type": "bs", "df": 5},                       # Fixed 5 df
        "BonusMalus": {"type": "linear", "monotonicity": "increasing"},  # Constrained coefficient
        "Region": {"type": "categorical"},
        "Brand": {"type": "target_encoding"},
        "Age2": {"type": "expression", "expr": "DrivAge**2"},
    },
    interactions=[
        {
            "VehAge": {"type": "linear"}, 
            "Region": {"type": "categorical"}, 
            "include_main": True
        },
    ],
    data=data,
    family="poisson",
    offset="Exposure",
    seed=42,
).fit(regularization="elastic_net")

Term Types

Type	Parameters	Description
`linear`	`monotonicity` (optional)	Raw continuous variable
`categorical`	`levels` (optional)	Dummy encoding
`bs`	`df` or `k`, `knots`, `boundary_knots`, `degree=3`, `monotonicity`	B-spline (default: penalized smooth, k=10)
`ns`	`df` or `k`, `knots`, `boundary_knots`	Natural spline (default: penalized smooth, k=10)
`target_encoding`	`prior_weight=1`	Regularized target encoding
`expression`	`expr`, `monotonicity` (optional)	Arbitrary expression (like `I()`)

Interactions

Each interaction is a dict with variable specs. Use include_main to also add main effects.

interactions=[
    # Standard interaction: product terms (main effects + interaction)
    {
        "DrivAge": {"type": "bs", "df": 5}, 
        "Brand": {"type": "target_encoding"},
        "include_main": True
    },
    # Categorical × continuous (interaction only)
    {
        "VehAge": {"type": "linear"}, 
        "Region": {"type": "categorical"}, 
        "include_main": False
    },
    # TE interaction: combined target encoding TE(Brand:Region)
    {
        "Brand": {"type": "categorical"},
        "Region": {"type": "categorical"},
        "target_encoding": True,
        "prior_weight": 1.0,  # optional
    },
    # FE interaction: combined frequency encoding FE(Brand:Region)
    {
        "Brand": {"type": "categorical"},
        "Region": {"type": "categorical"},
        "frequency_encoding": True,
    },
]

Flag	Effect
(none)	Standard product terms (cat×cat, cat×cont, etc.)
`target_encoding: True`	Combined TE encoding: `TE(var1:var2)`
`frequency_encoding: True`	Combined FE encoding: `FE(var1:var2)`

Splines

# Default: penalized smooth with automatic tuning via GCV
result = rs.glm_dict(
    response="ClaimNb",
    terms={
        "Age": {"type": "bs"},           # B-spline (auto-tuned)
        "VehPower": {"type": "ns"},      # Natural spline (auto-tuned)
        "Region": {"type": "categorical"},
    },
    data=data, family="poisson", offset="Exposure",
).fit()

# Fixed degrees of freedom (no penalty)
result = rs.glm_dict(
    response="ClaimNb",
    terms={
        "Age": {"type": "bs", "df": 5},       # Fixed 5 df
        "VehPower": {"type": "ns", "df": 4},  # Fixed 4 df
        "Region": {"type": "categorical"},
    },
    data=data, family="poisson", offset="Exposure",
).fit()

Spline parameters:

No parameters → penalized smooth with automatic tuning (k=10)
df=5 → fixed 5 degrees of freedom
k=15 → penalized smooth with 15 basis functions
knots=[2.0, 5.0, 8.0] → explicit interior knot positions (mutually exclusive with df/k)
boundary_knots=(0.0, 10.0) → custom boundary knots (optional, defaults to data range)
monotonicity="increasing" or "decreasing" → constrained effect (bs only)

When to use each type:

B-splines (bs): Standard choice, more flexible at boundaries, supports monotonicity
Natural splines (ns): Better extrapolation, linear beyond boundaries

Monotonic Splines

Constrain the fitted curve to be monotonically increasing or decreasing. Essential when business logic dictates a monotonic relationship.

# Monotonically increasing effect (e.g., age → risk)
result = rs.glm_dict(
    response="ClaimNb",
    terms={
        "Age": {"type": "bs", "monotonicity": "increasing"},
        "Region": {"type": "categorical"},
    },
    data=data, family="poisson", offset="Exposure",
).fit()

# Monotonically decreasing effect (e.g., vehicle value with age)
result = rs.glm_dict(
    response="ClaimAmt",
    terms={"VehAge": {"type": "bs", "df": 4, "monotonicity": "decreasing"}},
    data=data, family="gamma",
).fit()

Coefficient Constraints

Constrain coefficient signs using monotonicity on linear and expression terms.

result = rs.glm_dict(
    response="y",
    terms={
        "age": {"type": "linear", "monotonicity": "increasing"},  # β ≥ 0
        "age2": {"type": "expression", "expr": "age ** 2", "monotonicity": "decreasing"},  # β ≤ 0
        "income": {"type": "linear"},
    },
    data=data, family="poisson",
).fit()

Constraint	Term Spec	Effect
β ≥ 0	`"monotonicity": "increasing"`	Positive effect
β ≤ 0	`"monotonicity": "decreasing"`	Negative effect

Target Encoding

Ordered target encoding for high-cardinality categoricals.

# Dict API
result = rs.glm_dict(
    response="ClaimNb",
    terms={
        "Brand": {"type": "target_encoding"},
        "Model": {"type": "target_encoding", "prior_weight": 2.0},
        "Age": {"type": "linear"},
        "Region": {"type": "categorical"},
    },
    data=data, family="poisson", offset="Exposure",
).fit()

# Sklearn-style API
encoder = rs.TargetEncoder(prior_weight=1.0, n_permutations=4)
train_encoded = encoder.fit_transform(train_categories, train_target)
test_encoded = encoder.transform(test_categories)

Key benefits:

No target leakage: Ordered target statistics
Regularization: Prior weight controls shrinkage toward global mean
High-cardinality: Single column instead of thousands of dummies
Exposure-aware: For frequency models with offset="Exposure", automatically uses claim rate (ClaimCount/Exposure) instead of raw counts
Interactions: Use target_encoding: True in interactions to encode variable combinations

Expression Terms

result = rs.glm_dict(
    response="y",
    terms={
        "age": {"type": "linear"},
        "age2": {"type": "expression", "expr": "age ** 2"},
        "age3": {"type": "expression", "expr": "age ** 3"},
        "income_k": {"type": "expression", "expr": "income / 1000"},
        "bmi": {"type": "expression", "expr": "weight / (height ** 2)"},
    },
    data=data, family="gaussian",
).fit()

Supported operations: +, -, *, /, ** (power)

Regularization

CV-Based Regularization

# Just specify regularization type - cv=5 is automatic
result = rs.glm_dict(
    response="y",
    terms={"x1": {"type": "linear"}, "x2": {"type": "linear"}, "cat": {"type": "categorical"}},
    data=data,
    family="poisson",
).fit(regularization="ridge")  # "ridge", "lasso", or "elastic_net"

print(f"Selected alpha: {result.alpha}")
print(f"CV deviance: {result.cv_deviance}")

Options:

regularization: "ridge" (L2), "lasso" (L1), or "elastic_net" (mix)
selection: "min" (best fit) or "1se" (more conservative, default: "min")
cv: Number of folds (default: 5)

Explicit Alpha

# Skip CV, use specific alpha
result = rs.glm_dict(response="y", terms={"x1": {"type": "linear"}, "x2": {"type": "linear"}}, data=data).fit(alpha=0.1, l1_ratio=0.0)  # Ridge
result = rs.glm_dict(response="y", terms={"x1": {"type": "linear"}, "x2": {"type": "linear"}}, data=data).fit(alpha=0.1, l1_ratio=1.0)  # Lasso
result = rs.glm_dict(response="y", terms={"x1": {"type": "linear"}, "x2": {"type": "linear"}}, data=data).fit(alpha=0.1, l1_ratio=0.5)  # Elastic Net

Lasso Credibility

Shrink model coefficients toward a prior model (complement of credibility) instead of toward zero. Based on the methodology in CAS Monograph 13 (Holmes & Casotto, 2025).

When lasso zeroes a coefficient, the prediction for that term falls back to the complement rather than vanishing — making regularized models directly usable as rating plans.

# 1. Fit a countrywide (prior) model
cw_result = rs.glm_dict(
    response="ClaimCount",
    terms={"VehAge": {"type": "bs"}, "DrivAge": {"type": "bs"}},
    data=countrywide_data,
    family="poisson",
    offset="Exposure",
).fit()

# 2. Fit a state model with lasso, shrinking toward countrywide rates
state_result = rs.glm_dict(
    response="ClaimCount",
    terms={
        "VehAge": {"type": "bs"},
        "DrivAge": {"type": "bs"},
        "Region": {"type": "categorical"},
    },
    data=state_data,
    family="poisson",
    offset="Exposure",
    complement="countrywide_rate",  # Column with prior rates (response scale)
).fit(regularization="lasso")

# 3. Inspect which terms the data supports vs. trusts the complement
print(state_result.summary())            # Shows "Lasso Credibility Results"
print(state_result.credibility_summary())  # Deviation from complement per term

Complement sources:

str — column name in the DataFrame (rates for log-link, probabilities for logit)
np.ndarray — array of prior values on the response scale
GLMModel — fitted model; predictions are computed automatically

Works with all families/links, splines, categoricals, interactions, and target encoding.

Design Matrix Validation

# Check for issues before fitting
model = rs.glm_dict(
    response="y",
    terms={"x": {"type": "ns", "df": 4}, "cat": {"type": "categorical"}},
    data=data, family="poisson",
)
results = model.validate()  # Prints diagnostics

if not results['valid']:
    print("Issues:", results['suggestions'])

# Validation runs automatically on fit failure with helpful suggestions

Checks performed:

Rank deficiency (linearly dependent columns)
High multicollinearity (condition number)
Zero variance columns
NaN/Inf values
Highly correlated column pairs (>0.999)

Model Diagnostics

# Compute all diagnostics at once
diagnostics = result.diagnostics(
    data=data,
    categorical_factors=["Region", "VehBrand", "Area"],  # Including non-fitted
    continuous_factors=["Age", "Income", "VehPower"],    # Including non-fitted
)

# Export as compact JSON (optimized for LLM consumption)
json_str = diagnostics.to_json()

# Pre-fit data exploration (no model needed)
exploration = rs.explore_data(
    data=data,
    response="ClaimNb",
    categorical_factors=["Region", "VehBrand", "Area"],
    continuous_factors=["Age", "VehPower", "Income"],
    exposure="Exposure",
    family="poisson",
    detect_interactions=True,
)

Diagnostic Features:

Calibration: Overall A/E ratio, calibration by decile with CIs, Hosmer-Lemeshow test
Discrimination: Gini coefficient, AUC, KS statistic, lift metrics
Factor Diagnostics: A/E by level/bin for ALL factors (fitted and non-fitted)
VIF/Multicollinearity: Variance inflation factors for design matrix columns
Partial Dependence: Effect plots with shape detection and recommendations
Overfitting Detection: Compare train vs test metrics when test data provided
Interaction Detection: Greedy residual-based detection of potential interactions
Warnings: Auto-generated alerts for high dispersion, poor calibration, missing factors
Base Model Comparison: Compare new model against existing/benchmark predictions

Diagnostics JSON Shape

The diagnostics.to_json() output includes:

{
  "model_summary": {
    "formula": "...", "family": "poisson", "link": "log",
    "n_obs": 2000, "n_params": 6, "df_resid": 1994,
    "converged": true, "iterations": 5,
    "scale": 1.0,
    "scale_pearson": 1.0148,
    "null_deviance": 1408.77,
    "robust_se_type": "HC1"
  },
  "train_test": {
    "train": {
      "n_obs": 2000, "deviance": 2118.42,
      "log_likelihood": -1059.21,
      "aic": 2130.42, "bic": 2162.70,
      "gini": 0.3241, "auc": 0.6621, "ae_ratio": 1.0
    }
  },
  "coefficient_summary": [
    {
      "feature": "Age", "estimate": 0.00996,
      "std_error": 0.00339, "z_value": 2.941,
      "p_value": 0.0033, "significant": true,
      "conf_int": [0.003318, 0.01659],
      "relativity": 1.01, "relativity_ci": [1.0033, 1.0167],
      "robust_std_error": 0.003378, "robust_z_value": 2.947,
      "robust_p_value": 0.0032, "robust_significant": true
    }
  ]
}

BIC (train_test.train.bic): Bayesian Information Criterion alongside AIC
Scale (model_summary.scale): Deviance-based dispersion parameter
Scale Pearson (model_summary.scale_pearson): Pearson-based dispersion estimate
Null Deviance (model_summary.null_deviance): Intercept-only model deviance
Confidence Intervals (coefficient_summary[].conf_int): 95% CI [lower, upper]
Robust SEs (coefficient_summary[].robust_*): HC1 sandwich estimators for each coefficient
Robust SE Type (model_summary.robust_se_type): Present only when robust SEs were computed

Robust SE fields are null when store_design_matrix=False (lean mode) or for deserialized models.

Comparing Against a Base Model

Compare your new model against predictions from an existing model (e.g., current production model):

# Add base model predictions to your data
data = data.with_columns(pl.lit(old_model_predictions).alias("base_pred"))

# Run diagnostics with base_predictions
diagnostics = result.diagnostics(
    train_data=data,
    categorical_factors=["Region", "VehBrand"],
    continuous_factors=["Age", "VehPower"],
    base_predictions="base_pred",  # Column name with base model predictions
)

# Access comparison results
bc = diagnostics.base_predictions_comparison

# Side-by-side metrics
print(f"Model loss: {bc.model_metrics.loss}, Base loss: {bc.base_metrics.loss}")
print(f"Model Gini: {bc.model_metrics.gini}, Base Gini: {bc.base_metrics.gini}")

# Improvement metrics (positive = new model is better)
print(f"Loss improvement: {bc.loss_improvement_pct}%")
print(f"Gini improvement: {bc.gini_improvement}")
print(f"AUC improvement: {bc.auc_improvement}")

# Decile analysis sorted by model/base prediction ratio
for d in bc.model_vs_base_deciles:
    print(f"Decile {d.decile}: actual={d.actual:.4f}, "
          f"model={d.model_predicted:.4f}, base={d.base_predicted:.4f}")

The comparison includes:

Side-by-side metrics: Loss (mean deviance), Gini, AUC, A/E ratio for both models
Improvement metrics: loss_improvement_pct, gini_improvement, auc_improvement
Decile analysis: Data sorted by model/base ratio, showing where the new model diverges
Calibration comparison: Count of deciles where each model has better A/E

Model Serialization

Save and load fitted models for later use:

# Fit and save
model_bytes = result.to_bytes()

with open("model.bin", "wb") as f:
    f.write(model_bytes)

# Load later
with open("model.bin", "rb") as f:
    loaded = rs.GLMModel.from_bytes(f.read())

# Predict with loaded model
predictions = loaded.predict(new_data)

What's preserved:

Coefficients and feature names
Categorical encoding levels
Spline knot positions
Target encoding statistics
Formula, family, link function
Complement of credibility specification

Compact storage: Only prediction-essential state is stored (~KB, not MB).

Model Export (PMML & ONNX)

Export fitted models to standard formats for deployment — no extra dependencies required. PMML uses stdlib XML; ONNX protobuf serialization is implemented from scratch in Rust.

PMML

# Export to PMML 4.4 XML
pmml_xml = result.to_pmml()
result.to_pmml(path="model.pmml")

# Load & predict (consumer side)
# uv add pypmml
from pypmml import Model
pmml_model = Model.fromFile("model.pmml")

new_data = pl.DataFrame({"VehAge": [3, 5, 1], "Area": ["C", "A", "B"]})
preds = pmml_model.predict(new_data.to_dict(as_series=False))

ONNX

# Export — "scoring" requires pre-built design matrix, "full" embeds preprocessing
result.to_onnx(path="model.onnx", mode="scoring")
result.to_onnx(path="model_full.onnx", mode="full")

# Predict (consumer side)
# uv add onnxruntime
import onnxruntime as ort
session = ort.InferenceSession("model_full.onnx")
preds = session.run(None, {"input": raw_features})[0]

| Size | Smaller | Larger |

Dependencies

Rust

ndarray, nalgebra - Linear algebra
rayon - Parallel iterators (multi-threading)
statrs - Statistical distributions
pyo3 - Python bindings

Python

numpy - Array operations (required)
polars - DataFrame support (required)

License

AGPL-3.0

Project details

These details have been verified by PyPI

Project links

GitHub Statistics

Maintainers

PricingFrontier

These details have not been verified by PyPI

Release history Release notifications | RSS feed

This version

0.6.7

Apr 29, 2026

0.6.6

Apr 13, 2026

0.6.4

Apr 13, 2026

0.6.3

Mar 22, 2026

0.6.2

Mar 22, 2026

0.6.1

Mar 10, 2026

0.6.0

Mar 8, 2026

0.5.0

Feb 24, 2026

0.4.13

Feb 13, 2026

0.4.12

Feb 13, 2026

0.4.11

Feb 12, 2026

0.4.10

Feb 12, 2026

0.4.9

Feb 12, 2026

0.4.8

Feb 12, 2026

0.4.6

Feb 11, 2026

0.4.5

Feb 11, 2026

0.4.4

Feb 11, 2026

0.4.3

Feb 11, 2026

0.4.2

Feb 10, 2026

0.4.1

Feb 9, 2026

0.4.0

Feb 9, 2026

0.3.9

Feb 8, 2026

0.3.8

Jan 31, 2026

0.3.7

Jan 30, 2026

0.3.6

Jan 30, 2026

0.3.5

Jan 28, 2026

0.3.4

Jan 28, 2026

0.3.3

Jan 28, 2026

0.3.2

Jan 28, 2026

0.3.1

Jan 27, 2026

0.3.0

Jan 26, 2026

0.2.5

Jan 22, 2026

0.2.4

Jan 20, 2026

0.2.3

Jan 19, 2026

0.2.2

Jan 19, 2026

0.2.1

Jan 18, 2026

0.1.22

Jan 14, 2026

0.1.21

Jan 14, 2026

0.1.20

Jan 14, 2026

0.1.19

Jan 13, 2026

0.1.18

Jan 12, 2026

0.1.17

Jan 12, 2026

0.1.16

Jan 12, 2026

0.1.15

Jan 12, 2026

0.1.14

Jan 11, 2026

0.1.13

Jan 11, 2026

0.1.12

Jan 11, 2026

0.1.11

Jan 10, 2026

0.1.10

Jan 9, 2026

0.1.9

Jan 9, 2026

0.1.8

Jan 8, 2026

0.1.7

Jan 7, 2026

0.1.6

Jan 7, 2026

0.1.5

Jan 6, 2026

0.1.4

Jan 6, 2026

0.1.3

Jan 6, 2026

0.1.2

Jan 6, 2026

0.1.1

Jan 6, 2026

0.1.0

Jan 5, 2026

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

rustystats-0.6.7.tar.gz (422.8 kB view details)

Uploaded Apr 29, 2026 Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

rustystats-0.6.7-cp313-cp313-win_amd64.whl (1.2 MB view details)

Uploaded Apr 29, 2026 CPython 3.13Windows x86-64

rustystats-0.6.7-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.4 MB view details)

Uploaded Apr 29, 2026 CPython 3.13manylinux: glibc 2.17+ x86-64

rustystats-0.6.7-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.3 MB view details)

Uploaded Apr 29, 2026 CPython 3.13manylinux: glibc 2.17+ ARM64

rustystats-0.6.7-cp313-cp313-macosx_11_0_arm64.whl (1.3 MB view details)

Uploaded Apr 29, 2026 CPython 3.13macOS 11.0+ ARM64

rustystats-0.6.7-cp313-cp313-macosx_10_12_x86_64.whl (1.3 MB view details)

Uploaded Apr 29, 2026 CPython 3.13macOS 10.12+ x86-64

rustystats-0.6.7-cp312-cp312-win_amd64.whl (1.2 MB view details)

Uploaded Apr 29, 2026 CPython 3.12Windows x86-64

rustystats-0.6.7-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.4 MB view details)

Uploaded Apr 29, 2026 CPython 3.12manylinux: glibc 2.17+ x86-64

rustystats-0.6.7-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.3 MB view details)

Uploaded Apr 29, 2026 CPython 3.12manylinux: glibc 2.17+ ARM64

rustystats-0.6.7-cp312-cp312-macosx_11_0_arm64.whl (1.3 MB view details)

Uploaded Apr 29, 2026 CPython 3.12macOS 11.0+ ARM64

rustystats-0.6.7-cp312-cp312-macosx_10_12_x86_64.whl (1.3 MB view details)

Uploaded Apr 29, 2026 CPython 3.12macOS 10.12+ x86-64

rustystats-0.6.7-cp311-cp311-win_amd64.whl (1.2 MB view details)

Uploaded Apr 29, 2026 CPython 3.11Windows x86-64

rustystats-0.6.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.4 MB view details)

Uploaded Apr 29, 2026 CPython 3.11manylinux: glibc 2.17+ x86-64

rustystats-0.6.7-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.3 MB view details)

Uploaded Apr 29, 2026 CPython 3.11manylinux: glibc 2.17+ ARM64

rustystats-0.6.7-cp311-cp311-macosx_11_0_arm64.whl (1.3 MB view details)

Uploaded Apr 29, 2026 CPython 3.11macOS 11.0+ ARM64

rustystats-0.6.7-cp311-cp311-macosx_10_12_x86_64.whl (1.3 MB view details)

Uploaded Apr 29, 2026 CPython 3.11macOS 10.12+ x86-64

rustystats-0.6.7-cp310-cp310-win_amd64.whl (1.2 MB view details)

Uploaded Apr 29, 2026 CPython 3.10Windows x86-64

rustystats-0.6.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.4 MB view details)

Uploaded Apr 29, 2026 CPython 3.10manylinux: glibc 2.17+ x86-64

rustystats-0.6.7-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.3 MB view details)

Uploaded Apr 29, 2026 CPython 3.10manylinux: glibc 2.17+ ARM64

rustystats-0.6.7-cp310-cp310-macosx_11_0_arm64.whl (1.3 MB view details)

Uploaded Apr 29, 2026 CPython 3.10macOS 11.0+ ARM64

rustystats-0.6.7-cp310-cp310-macosx_10_12_x86_64.whl (1.3 MB view details)

Uploaded Apr 29, 2026 CPython 3.10macOS 10.12+ x86-64

File details

Details for the file rustystats-0.6.7.tar.gz.

File metadata

Download URL: rustystats-0.6.7.tar.gz
Upload date: Apr 29, 2026
Size: 422.8 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for rustystats-0.6.7.tar.gz
Algorithm	Hash digest
SHA256	`6872d6c787f18f252becf285709cf645991647cbeba69ad134147eea2cdaa9f1`
MD5	`2958e853e3859b5c611a7aa99c08dc4e`
BLAKE2b-256	`e3d2de99d33f5d486d20dc56fd4f363f85b1a97e36d9a4ffed3a57e23bcc96cc`

Algorithm	Hash digest
SHA256	`e1fc3b84dc711c9259cf18583833b524901e5d65bc0bc3f8c699af00075b975d`
MD5	`05150a01a77413e77ec7c9ba5415c29f`
BLAKE2b-256	`992562bb0bba101540d1fb0f352d817f3ecf380426d86b9791494257ab1052ac`

Algorithm	Hash digest
SHA256	`af29a5bfea22bf19065410d8ca30a3db8d95b3aa30fef92b328bcd4347d35dba`
MD5	`14c12ba0a313818a2578f4450fa3e507`
BLAKE2b-256	`aca0ee5b0aa126e298c2d3b9a4dda792222b50e979a147eb87ec6954da01ed07`

Algorithm	Hash digest
SHA256	`683e801c91255f8a10412741dda201cd0e03440f5125b31906953b02f5d6f9e8`
MD5	`dba10fbf2a2bd08956a657fbc02e9c55`
BLAKE2b-256	`98c3e4bc569f4406a4f4400c4b4189f332b0b423aede1b6500c7d7bc4843a91d`

Algorithm	Hash digest
SHA256	`f5a1f8a191ac0789f83013c3039f9f69fb99ecf0c92263320cde78028ca7e1cb`
MD5	`60e8d9106988845c1942830c94fea716`
BLAKE2b-256	`acb540d039bc64be971e5376ff9a9d93288874bde9c3ed49467d5527b2e10875`

Algorithm	Hash digest
SHA256	`46cfba6eb1bb7cdf9bb1022940faf7b3c36a9228e371e10ac7758a6b9dc7bae6`
MD5	`4f52aa2e2fe850880e59903fee6de25b`
BLAKE2b-256	`fc714445cf1d06b3bbfb7e1a823e30e11afc802cdafa3002e420b72740a07132`

Algorithm	Hash digest
SHA256	`e755aca286ac33ec491d077f8e15a5372cf5184e2d381eb10b2952ca5e182c59`
MD5	`dffd28b9160725a97fc813a5b99dbd8d`
BLAKE2b-256	`d942383a715382a6fbe9fd7b5f635fe45af5d3e97aca87b5048f73fb9e7e73f2`

Algorithm	Hash digest
SHA256	`06e53090ba7fdb4c54ddb5d0ec5ef68eb1a923a709df99f15508b5a3b00b88e9`
MD5	`0600fb609acd72f224e09d8b2c6633d4`
BLAKE2b-256	`18f071d74e84c9924c873faabe9555b679ba974b8569ea4e7db20cb4f1ecf012`

Algorithm	Hash digest
SHA256	`0fa7e61351b5c8bdca9d418f7b5fd81e641221ecd8a22fb569b95d237acdb31e`
MD5	`a5a9768275e6743ef322de90e2444c3d`
BLAKE2b-256	`c523379b05671e5bf120de0b613d6b9fc1c78b1470c4c7ad6d210cbcc0e30f19`

Algorithm	Hash digest
SHA256	`fbef28fb6a1745df2471194827f8e7fe989825a395e2ac6ed7c15a533d649743`
MD5	`657fd5ff6d5416ac5b2f57b8bb56aab2`
BLAKE2b-256	`21ad8e6611cddc7142da1ce048aa4f144667b5accdc7167097cf2d1dabbc4496`

Algorithm	Hash digest
SHA256	`7164942732029d46aee1d10797bc903b0fcaa099891005affc8149b5fdbf335c`
MD5	`0bc7480b0dcbbfd53df84d4afcc548e1`
BLAKE2b-256	`8c4b04e6a50c843fd5fa5581127e602e4fef2d447ac06cefcd8d00f75eb5c1cb`

Algorithm	Hash digest
SHA256	`0f00f3a943334c22fdf52e48bb07b7da52f6145983dbb3e7c262204332ac6987`
MD5	`23bb073576f713cf29fc219a8e35f548`
BLAKE2b-256	`8726ac98f5531093b34a0fab00d175bb24185e729e2252e51ad65c54958ed367`

Algorithm	Hash digest
SHA256	`8a1e6cc91c90df3494bc611c41bc4a4fcd68ee48425966084ae8a4fa79c50fba`
MD5	`ca4c29116251e6cd89837e4e8a3e3644`
BLAKE2b-256	`055dc9822447f1e9d8f4b0600dbd46dc0033fbf0d139152afcc530cea13e7dcf`

Algorithm	Hash digest
SHA256	`0942d5e7ec558bd09839fbd440d8ec2bcc70fb778a72fcd0611b00d8f4eeb227`
MD5	`918a7867a4f9ea40c79f876002b447b6`
BLAKE2b-256	`eee40ac4b464976b90372a37ffe9680ecd79130d2c4c91e57e561230ef0fc9f5`

Algorithm	Hash digest
SHA256	`6df23e0f1990ecbc13e234f8cc8ab6e1438d6489b53dcdba98387e53d36345d5`
MD5	`6dc6aec20e9f8b9493a0effede3f373a`
BLAKE2b-256	`5e4cc420c47c5f29c4e52d8fef24232db8c703501a4fbe0216a37977fe5cb68f`

Algorithm	Hash digest
SHA256	`ff15d0085b0f6be779e17d4a4991102e1c07260dd42986b982026c2900b47fe2`
MD5	`7f191ecc4b230a3de8642bc397c20af6`
BLAKE2b-256	`322c3a4f6b7a47eab5f246985963472cf0d87ee4426992fdf289f9891d46ced3`

Algorithm	Hash digest
SHA256	`5687ba468b8427fe56b5263ed2bb00a93956005dfa82725c0ba29881a75d8ea8`
MD5	`c8f4232bafd151e08f6f95fc3ff44bc4`
BLAKE2b-256	`d37b438bb748733035a47a58554a689b4a1389ce2efc8fcca3a6a203a1ccb8f7`

Algorithm	Hash digest
SHA256	`b86e943010b3bc094645a7d88cfd1ff260fb36044b619561caa222cea85a3650`
MD5	`ecc26c1aa88430241d5b1ab6a7479507`
BLAKE2b-256	`db4262d5fda4d6821290d8effa99946d0f45854287b8668c070b1976e4624765`

Algorithm	Hash digest
SHA256	`daea67653fe414d7ec37093a054fcd6a63b8a31d5d0c872355ccb7308bad55d4`
MD5	`fa1cf45e0e82a6108f412e1b6c5ab9ec`
BLAKE2b-256	`b09fda0b7989606fce794eb7ae5bff07dfddc8326f80eb2bc021812d1872647d`

Algorithm	Hash digest
SHA256	`a0f1db5cbd6e932eaa9506eb13374f6e9943132ffc4a8315936bfe90d4ce2d6e`
MD5	`26c65e66031319a43cecfb4a1ca88720`
BLAKE2b-256	`d1b1050585d39c52b8e852cf3cae8813ed3606e0e4a11df8f0709f7201cee07a`

Algorithm	Hash digest
SHA256	`94e0d912bd2c043a619866a3f4157cee152868ed5f3c00ae14bbe776bde92e3b`
MD5	`f3e50269ac998222529deef1d295cf42`
BLAKE2b-256	`ae223a797371c97e9ab6750332f54387f63c9d40adf29127d48692526a50575b`

rustystats 0.6.7

Navigation

Verified details

Project links

GitHub Statistics

Maintainers

Unverified details

Meta

Classifiers

Project description

RustyStats 🦀📊

Features

Installation

Quick Start

Families & Links

Dict-Based API

Term Types

Interactions

Splines

Monotonic Splines

Coefficient Constraints

Target Encoding

Expression Terms

Regularization

CV-Based Regularization

Explicit Alpha

Lasso Credibility

Design Matrix Validation

Model Diagnostics

Diagnostics JSON Shape

Comparing Against a Base Model

Model Serialization

Model Export (PMML & ONNX)

PMML

ONNX

Dependencies

Rust

Python

License

Project details

Verified details

Project links

GitHub Statistics

Maintainers

Unverified details

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details

File metadata

File hashes

Provenance

File details