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csdm 2.0.0

Version 2.0.0 expands the package’s R model interfaces, adds new sample controls, and improves estimator and diagnostic behavior. It also includes bug fixes for edge cases involving irregular time indexes, missing observations, cross-sectional averages, covariance calculations, and dependence tests. For reproducibility, report the package version when comparing output across releases.

Estimation and inference

• Add fullsample = TRUE for CCE-based models. Cross-sectional averages are then calculated variable by variable from all finite observations in the selected sample before dynamic lag trimming.
• Construct model and cross-sectional-average lags by explicit time-grid matching, preventing lags from crossing gaps in a panel.
• Evaluate formulas, transformed cross-sectional-average variables, subsets, and missing-value policies consistently.
• Correct missing-value and leave-one-out cross-sectional averages.
• Use one eligible unit sample for mean-group coefficients and covariance, and require at least two eligible units.
• Correct fixed-weight mean-group covariance scaling and HC0–HC3 sandwich covariance calculations.
• Check structural identification after cross-sectional-average projection and report excluded units.
• Align CS-ARDL parameter components and covariance, and expose unit-level AR stability and long-run-ratio eligibility.
• Validate panel keys, retain original observation identity when sorting, and preserve named cross-sectional-average lag specifications.

Dependence diagnostics

• Remove time periods containing no estimated residuals before assessing CD sample balance, while retaining the selected policy for partially observed periods.
• Implement the paper-defined pooled-variance CDw statistic and the correlation-scale screening term used by CDw+ for balanced samples.
• Correct CD-star unit-specific residual scales and validate PCA rank.
• Make randomized diagnostics opt-in, preserve seeded RNG state, and use pairwise samples for the classical CD statistic.
• Validate clustered covariance inputs and make residual transformations explicit.

R interfaces

• Add standard extraction and update methods, original-row fitted and residual outputs, and stored-data model updates.
• Add tidy(), glance(), and augment() methods with inference and row-alignment checks.
• Document cross_sectional_avg() as a supported standalone data utility and distinguish it from averages configured by csdm_csa().
• Retain evaluated subset, time-spacing, missing-value, and pdata.frame time information when models are updated.
• Reject unsupported model, trend, cross-sectional-average, long-run, and covariance specifications instead of silently storing or ignoring them.

Deprecations

• Deprecate csdm_pooled() because pooled restrictions are not implemented.
• Deprecate get_residuals() in favor of the standard residuals() method.
• Deprecate prepare_cd_input() because its transformations are not used by cd_test() and can change the hypothesis represented by the residuals.
• Deprecate the exported cluster_vcov(), sandwich_vcov(), and pooled_vcov() utilities. Existing calls continue to work with a migration warning.

Documentation and maintenance

• Rewrite the README, introductory vignette, and pkgdown navigation around the implemented API. Correct the description of log_ngd, update the CDw/CDw+ explanations, and make the introductory examples runnable.
• Add reference fixtures, statistical validation scripts, platform checks, and Codecov reporting.
• Share unit-regression and sample-bookkeeping code across the MG, CCE, and DCCE engines, and remove obsolete internal helpers and imports.

csdm 1.0.1

Documentation and reference enhancements

• Add references for the implemented estimators and methods, including key papers and textbooks.
• Improve function documentation, estimator descriptions, assumptions, and documentation consistency.
• Add a link for reporting bugs.

csdm 1.0.0

Initial CRAN release

Estimators

• Mean Group (MG)
• Common Correlated Effects (CCE)
• Dynamic CCE (DCCE)
• Cross-Sectionally Augmented ARDL (CS-ARDL)

Inference and diagnostics

• Cross-sectional dependence (CD) tests
• Summary and printing methods

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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