Functions for nonlinear regression parameters estimation by algorithms based on Controlled Random Search algorithm.
  Both functions (crs4hc(), crs4hce()) adapt current search strategy by four heuristics competition. In addition, crs4hce() improves adaptability by adaptive stopping condition.

Choose the appropriate map projection for a given application,
  visualise the resulting distortion, and georeference data from unknown
  projections. The full functionalities of the package are described in
  Pozzi et al. (2026) <doi:10.31223/X5K17P> (pre-print).

Uses data from the 'EPSG' Registry to look up suitable coordinate reference 
    system transformations for spatial datasets in R.  Returns a data frame with 'CRS' codes
    that can be used for 'CRS' transformation and mapping projects.  Please see 
    the 'EPSG' Dataset Terms of Use at <https://epsg.org/terms-of-use.html> for more information.

Provides methods for powering cluster-randomized trials with two continuous co-primary outcomes using five key design techniques. Includes functions for calculating required sample size and statistical power. For more details on methodology, see Owen et al. (2025) <doi:10.1002/sim.70015>, Yang et al. (2022) <doi:10.1111/biom.13692>, Pocock et al. (1987) <doi:10.2307/2531989>, Vickerstaff et al. (2019) <doi:10.1186/s12874-019-0754-4>, and Li et al. (2020) <doi:10.1111/biom.13212>.

Computes p-value according to the CRT using the HierNet test statistic. For more details, see Ham, Imai, Janson (2022) "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" <arXiv:2201.08343>.

Implements a semi-parametric GEE estimator accounting for missing data with Inverse-probability weighting (IPW) and for imbalance in covariates with augmentation (AUG). The estimator IPW-AUG-GEE is Doubly robust (DR).

Sample size estimation in cluster (group) randomized trials.  Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012).