python-gls 0.1.0

GLS estimator with learned correlation and variance structures (Python equivalent of R's nlme::gls)

python-gls

GLS with learned correlation and variance structures for Python.

The missing Python equivalent of R's nlme::gls(). Unlike statsmodels.GLS (which requires you to supply a pre-computed covariance matrix), python-gls estimates the correlation and variance structure from your data via maximum likelihood (ML) or restricted maximum likelihood (REML) — exactly like R's nlme::gls().

Why this library?

If you work with panel data, repeated measures, longitudinal studies, or clustered observations, your errors are probably correlated and possibly heteroscedastic. Ignoring this gives you wrong standard errors and misleading p-values.

R has had nlme::gls() for 25+ years. Python hasn't had an equivalent. Until now.

Feature	statsmodels.GLS	python-gls	R nlme::gls
Estimate correlation from data	No (manual Omega)	Yes	Yes
AR(1), compound symmetry, etc.	No	Yes (11 structures)	Yes
Heteroscedastic variance models	No	Yes (6 functions)	Yes
ML / REML estimation	No	Yes	Yes
R-style formulas	No	Yes	Yes

Installation

pip install python-gls

Or from source:

git clone https://github.com/brunoabrahao/python-gls.git
cd python-gls
pip install -e ".[dev]"

Quick Start

from python_gls import GLS
from python_gls.correlation import CorAR1
from python_gls.variance import VarIdent

result = GLS.from_formula(
    "response ~ treatment + time",
    data=df,
    correlation=CorAR1(),       # Learn AR(1) correlation
    variance=VarIdent("group"),  # Learn group-specific variances
    groups="subject",            # Define independent clusters
).fit()

print(result.summary())
print(f"Estimated AR(1) phi: {result.correlation_params[0]:.3f}")

Output:

==============================================================================
                      Generalized Least Squares Results
==============================================================================
Method:          REML                 Log-Likelihood:        -615.0544
No. Observations:500                  AIC:                   1240.1088
Df Model:        2                    BIC:                   1261.1818
Df Residuals:    497                  Sigma^2:                0.984576
Converged:       Yes                  Iterations:                    6
------------------------------------------------------------------------------
                           coef    std err          t      P>|t|     [0.025     0.975]
------------------------------------------------------------------------------
           Intercept     1.0368     0.1069     9.7013     0.0000     0.8268     1.2468
           treatment     0.6465     0.1428     4.5272     0.0000     0.3659     0.9271
                   x     1.9734     0.0323    61.0960     0.0000     1.9099     2.0368
==============================================================================
Correlation Structure: CorAR1
  Parameters: [0.61312872]

Correlation Structures

All correlation structures are in python_gls.correlation.

Temporal / Serial Correlation

Class	R Equivalent	Parameters	Description
CorAR1(phi=None)	corAR1()	1	First-order autoregressive. R[i,j] = phi^|i-j|
CorARMA(p, q)	corARMA(p, q)	p + q	ARMA(p,q) autocorrelation
CorCAR1(phi=None)	corCAR1()	1	Continuous-time AR(1) for irregular spacing
CorCompSymm(rho=None)	corCompSymm()	1	Exchangeable / compound symmetry. All pairs equal rho
CorSymm(dim=None)	corSymm()	d(d-1)/2	General unstructured. Free correlation for every pair

Spatial Correlation

Class	R Equivalent	Parameters	Description
CorExp(range_param, nugget=False)	corExp()	1-2	Exponential: exp(-d/range)
CorGaus(range_param, nugget=False)	corGaus()	1-2	Gaussian: exp(-(d/range)^2)
CorLin(range_param, nugget=False)	corLin()	1-2	Linear: max(1 - d/range, 0)
CorRatio(range_param, nugget=False)	corRatio()	1-2	Rational quadratic: 1/(1 + (d/range)^2)
CorSpher(range_param, nugget=False)	corSpher()	1-2	Spherical: cubic polynomial, zero beyond range

All spatial structures accept an optional nugget=True parameter for a discontinuity at distance zero.

Usage

from python_gls.correlation import CorAR1, CorSymm, CorExp

# Serial: AR(1) with optional initial value
cor = CorAR1(phi=0.5)

# Unstructured: all pairs free
cor = CorSymm()  # dimension inferred from data

# Spatial: set coordinates per group
cor = CorExp(range_param=10.0, nugget=True)
cor.set_coordinates(group_id=0, coords=np.array([[0,0], [1,0], [0,1]]))

Variance Functions

All variance functions are in python_gls.variance.

Class	R Equivalent	Parameters	Description
VarIdent(group_var)	varIdent(form=~1|group)	G-1	Different variance per group level
VarPower(covariate)	varPower(form=~cov)	1	sd = |v|^delta
VarExp(covariate)	varExp(form=~cov)	1	sd = exp(delta * v)
VarConstPower(covariate)	varConstPower(form=~cov)	2	sd = (c + |v|^delta)
VarFixed(weights_var)	varFixed(~cov)	0	Pre-specified weights (not estimated)
VarComb(*varfuncs)	varComb(...)	sum	Product of multiple variance functions

Usage

from python_gls.variance import VarIdent, VarPower, VarComb

# Different variance for treatment vs. control
var = VarIdent("treatment_group")

# Variance increases with fitted values
var = VarPower("fitted_values")

# Combine: group-specific + covariate-dependent
var = VarComb(VarIdent("group"), VarPower("x"))

API Reference

GLS Class

Construction

# From formula (recommended)
model = GLS.from_formula(
    formula,          # R-style formula: "y ~ x1 + x2"
    data,             # pandas DataFrame
    correlation=None, # CorStruct instance
    variance=None,    # VarFunc instance
    groups=None,      # str: column name for groups
    method="REML",    # "ML" or "REML"
)

# From arrays
model = GLS(
    endog=y,          # response vector
    exog=X,           # design matrix (include intercept column)
    correlation=None,
    variance=None,
    groups=None,      # array of group labels
    method="REML",
)

Fitting

result = model.fit(
    maxiter=200,     # max optimization iterations
    tol=1e-8,        # convergence tolerance
    verbose=False,   # print optimization progress
)

GLSResults Class

Property / Method	Type	Description
params	Series	Estimated coefficients
bse	Series	Standard errors
tvalues	Series	t-statistics
pvalues	Series	Two-sided p-values
conf_int(alpha=0.05)	DataFrame	Confidence intervals
sigma2	float	Estimated residual variance
loglik	float	Log-likelihood at convergence
aic	float	Akaike Information Criterion
bic	float	Bayesian Information Criterion
resid	array	Residuals (y - X*beta)
fittedvalues	array	Fitted values (X*beta)
correlation_params	array	Estimated correlation parameters
variance_params	array	Estimated variance parameters
cov_params_func()	DataFrame	Covariance matrix of beta
summary()	str	Formatted results table
converged	bool	Optimization convergence status
n_iter	int	Number of iterations
method	str	"ML" or "REML"

How It Works

The Statistical Model

GLS models the response as:

y = X*beta + epsilon, where Var(epsilon) = sigma^2 * Omega

The covariance matrix Omega is block-diagonal by group:

Omega_g = A_g^{1/2} R_g A_g^{1/2}

where:

• R_g is the correlation matrix (from the correlation structure)
• A_g is a diagonal matrix of variance weights (from the variance function)

Estimation

1. OLS initial fit to get starting residuals
2. Initialize correlation and variance parameters from residuals
3. Optimize profile log-likelihood over correlation/variance parameters using L-BFGS-B. At each step, beta and sigma^2 are profiled out analytically.
4. Compute final GLS estimates at the converged parameters:
   • beta = (X' Omega^{-1} X)^{-1} X' Omega^{-1} y
   • Cov(beta) = sigma^2 (X' Omega^{-1} X)^{-1}

Key Design Decisions

Spherical parametrization for CorSymm: The unstructured correlation matrix is parametrized via angles that map to a Cholesky factor, guaranteeing positive-definiteness without constrained optimization. Based on Pinheiro & Bates (1996).

Block-diagonal inversion: Omega is inverted per-group (O(n*m^3)) rather than as a full matrix (O(N^3)), where n = number of groups and m = group size.

REML: Restricted maximum likelihood integrates out the fixed effects from the likelihood, giving unbiased variance estimates. This is the default, matching R's nlme::gls().

Formula Syntax

Powered by formulaic, supporting:

# Simple linear
"y ~ x1 + x2"

# Categorical variables
"y ~ C(treatment)"

# Interactions
"y ~ x1 * x2"          # x1 + x2 + x1:x2
"y ~ x1 : x2"          # just the interaction

# Transformations
"y ~ np.log(x1) + x2"

# Remove intercept
"y ~ x1 + x2 - 1"

ML vs. REML

	ML	REML
Variance estimate	Biased (divides by N)	Unbiased (divides by N-k)
Default in R's gls	No	Yes
Default here	No	Yes
Use for model comparison	AIC/BIC of nested & non-nested models	Only models with same fixed effects
method=	"ML"	"REML"

Translating from R

R code → Python equivalent

# R
library(nlme)
m <- gls(y ~ x1 + x2,
         data = df,
         correlation = corAR1(form = ~1|subject),
         weights = varIdent(form = ~1|group),
         method = "REML")
summary(m)
intervals(m)

# Python
from python_gls import GLS
from python_gls.correlation import CorAR1
from python_gls.variance import VarIdent

r = GLS.from_formula(
    "y ~ x1 + x2",
    data=df,
    correlation=CorAR1(),
    variance=VarIdent("group"),
    groups="subject",
    method="REML",
).fit()

print(r.summary())
print(r.conf_int())

Parameter name mapping

R	Python	Notes
corAR1(form=~1|subject)	CorAR1(), groups="subject"	Groups specified at model level
corCompSymm(form=~1|id)	CorCompSymm(), groups="id"
corSymm(form=~1|id)	CorSymm(), groups="id"
corExp(form=~x+y|id)	CorExp(); cor.set_coordinates(...)	Coordinates set per group
varIdent(form=~1|group)	VarIdent("group")	Group variable as string
varPower(form=~fitted)	VarPower("fitted")	Covariate name as string
method="REML"	method="REML"	Same

Dependencies

• numpy >= 1.24
• scipy >= 1.10
• pandas >= 2.0
• formulaic >= 1.0

Testing

pip install -e ".[dev]"
pytest

License

MIT