Package: infometrics 0.3.0

infometrics: Information-Theoretic Methods for Econometric Estimation

Implements the class of Information-Theoretic (IT) estimators for econometric models, following the unified framework of Golan (2008) <doi:10.1561/0800000004>. Provides Generalized Maximum Entropy (GME) and Generalized Cross-Entropy (GCE) estimators for linear regression, instrumental variables, one-way error-component panel data, multinomial response, matrix balancing, and first-order Markov transition matrices, together with pure and noisy inverse-problem solvers. All estimators use the concentrated (dual) formulation for computational efficiency and report normalized-entropy, entropy-ratio, and Fano-bound diagnostics.

Authors:Ganbaatar Jambal [aut, cre]

infometrics_0.3.0.tar.gz
infometrics_0.3.0.zip(r-4.7-any)infometrics_0.3.0.zip(r-4.6-any)infometrics_0.3.0.zip(r-4.5-any)
infometrics_0.3.0.tgz(r-4.6-any)infometrics_0.3.0.tgz(r-4.5-any)
infometrics_0.3.0.tar.gz(r-4.7-any)infometrics_0.3.0.tar.gz(r-4.6-any)
infometrics_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
infometrics/json (API)

# Install 'infometrics' in R:
install.packages('infometrics', repos = c('https://genmaxent.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/genmaxent/infometrics_r/issues

On CRAN:

Conda:

cross-entropyeconometricsinformation-theorymaximum-entropystatistics

4.60 score 1 scripts 367 downloads 17 exports 0 dependencies

Last updated from:84a162357a. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK100
source / vignettesOK227
linux-releaseOK104
macos-releaseOK145
macos-oldrelOK158
windows-develOK86
windows-releaseOK79
windows-oldrelOK73
wasm-releaseOK75

Exports:default_supportsfano_boundsinverse_ceinverse_noiselinreglinreg_ivmake_supportmarginsmarkov_cemarkov_gcematrix_cematrix_gcemixed_gcemultinomial_gcenormalize_datapanel_gceshannon_entropy

Dependencies:

Generalized Maximum Entropy and Cross-Entropy for the Linear Model
Ill-conditioned regression problems | The GME formulation | From the primal to the concentrated dual | The GCE extension: incorporating prior information | Worked examples | A collinear design: GME versus OLS | Adding prior information (GCE) | The effect of $\nu$ | When the supports are too narrow | Inference: entropy diagnostics and hypothesis tests | Normalized entropy and the pseudo-$R^2$ | The entropy-ratio (ER) test | The Wald test | Instrumental variables with linreg_iv() | Practical guidance | References
Rendered fromgme-gce-regression.Rmdusingknitr::rmarkdown

Last update: 2026-08-30
Started: 2026-07-27

Pure Inverse Problems: Maximum Entropy and Cross-Entropy
The pure inverse problem | Maximum entropy | Solving with inverse_ce() | Cross-entropy: incorporating a prior | The ME special case, and a genuine prior | The information matrix and standard errors | Curvature-based standard errors from inverse_ce() | Normalized entropy and Fano bounds | From exact to noisy moments | References
Rendered frominverse-problems.Rmdusingknitr::rmarkdown

Last update: 2026-07-13
Started: 2026-07-13

Readme and manuals

Help Manual

Help pageTopics
Default Support Spaces from OLSdefault_supports
Information-Theoretic (Fano) Error Boundsfano_bounds
Fano Error Bounds for a Pure Inverse-Problem (Cross-Entropy) Fitfano_bounds.inverse_ce
Fano Error Bounds for a Noisy Inverse-Problem (GME/GCE) Fitfano_bounds.inverse_noise
Fano Error Bounds for a Mixed GCE Fitfano_bounds.mixed_gce
Fano Error Bounds for a Multinomial GCE Fitfano_bounds.multinomial_gce
Pure Inverse Problem via Cross-Entropy (Formula Interface)coef.inverse_ce fitted.inverse_ce inverse_ce print.inverse_ce print.summary.inverse_ce residuals.inverse_ce summary.inverse_ce vcov.inverse_ce
Noisy Inverse Problem via Generalized Cross-Entropy (Formula Interface)coef.inverse_noise fitted.inverse_noise inverse_noise print.inverse_noise print.summary.inverse_noise residuals.inverse_noise summary.inverse_noise vcov.inverse_noise
Generalized Cross-Entropy Linear Regression (Formula Interface)coef.linreg fitted.linreg linreg predict.linreg print.linreg print.summary.linreg residuals.linreg summary.linreg vcov.linreg
Stochastic-Moments GME Estimator for IV Regressionlinreg_iv
Construct a Symmetric Support Spacemake_support
Marginal Effectsmargins
Marginal Effects for a Mixed GCE Fitmargins.mixed_gce
Marginal Effects for a Multinomial GCE Fitmargins.multinomial_gce
Cross-Entropy Estimation of a Markov Transition/Balancing Matrix (Panel)coef.markov_ce fano_bounds.markov_ce fitted.markov_ce margins.markov_ce markov_ce print.markov_ce residuals.markov_ce summary.markov_ce
Generalized Cross-Entropy Estimation of a Markov Matrix with Noisy Momentscoef.markov_gce fano_bounds.markov_gce fitted.markov_gce margins.markov_gce markov_gce print.markov_gce residuals.markov_gce summary.markov_gce
Cross-Entropy Matrix Balancingcoef.matrix_ce fitted.matrix_ce matrix_ce print.matrix_ce print.summary.matrix_ce residuals.matrix_ce summary.matrix_ce
Generalized Cross-Entropy Matrix Balancing (Stochastic Moments)coef.matrix_gce fitted.matrix_gce matrix_gce print.matrix_gce print.summary.matrix_gce residuals.matrix_gce summary.matrix_gce
Doubly-Reparameterized Generalized Cross-Entropy Mixed Modelmixed_gce
nu-Weighted Generalized Cross-Entropy Multinomial Estimator (matrix interface)multinomial_gce
Normalize a numeric vector to [0, 1]normalize_data
Dual GCE Estimator for the One-Way Error-Components Panel Modelpanel_gce
Shannon Entropyshannon_entropy