Provides estimators and utilities for large panel-data models with cross-sectional dependence, including mean group (MG), common correlated effects (CCE) and dynamic CCE (DCCE) estimators, and cross-sectionally augmented ARDL (CS-ARDL) specifications, plus related inference and diagnostics.

Statistical downscaling and bias correction of climate predictions.
    It includes implementations of commonly used methods such as Analogs,
    Linear Regression, Logistic Regression, and Bias Correction techniques,
    as well as interpolation functions for regridding and point-based applications.
    It facilitates the production of high-resolution and local-scale climate
    information from coarse-scale predictions, which is essential for impact analyses.
    The package can be applied in a wide range of sectors and studies,
    including agriculture, water management, energy, heatwaves, and other
    climate-sensitive applications. The package was developed within the framework of
    the European Union Horizon Europe projects Impetus4Change (101081555) and ASPECT (101081460),
    the Wellcome Trust supported HARMONIZE project (224694/Z/21/Z), and the Spanish national project
    BOREAS (PID2022-140673OA-I00). Implements the methods described in
    'Ramon et al. (2021) <doi:10.1088/1748-9326/abe491>',
    'Duzenli et al. (2024) <doi:10.5194/egusphere-egu24-19420>',
    'Moreno-Montes et al. (2026) <doi:10.1016/j.cliser.2026.100639>',
    'Duzenli et al. (2026) <doi:10.1038/s41598-026-45067-2>',
    'Duzenli et al. (2026) <doi:10.1088/1748-9326/ae5c22>'.

Estimate, assess, test, and study linear, nonlinear, hierarchical 
  and multigroup structural equation models using composite-based approaches 
  and procedures, including estimation techniques such as partial least squares 
  path modeling (PLS-PM) and its derivatives (PLSc, ordPLSc, robustPLSc), 
  generalized structured component analysis (GSCA), generalized structured 
  component analysis with uniqueness terms (GSCAm), generalized canonical 
  correlation analysis (GCCA), principal component analysis (PCA), 
  factor score regression (FSR) using sum score, regression or 
  Bartlett scores (including bias correction using Croon’s approach), 
  as well as several tests and typical postestimation procedures 
  (e.g., verify admissibility of the estimates, assess the model fit, 
  test the model fit etc.).