Point-scale variogram deconvolution from irregular/regular spatial support according to Goovaerts, P., (2008) <doi: 10.1007/s11004-007-9129-1>; ordinary area-to-area (co)Kriging and area-to-point (co)Kriging.

Estimation and inference methods for bounding average treatment effects (on the treated) that are valid under an unconfoundedness assumption. 
    The bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated. 
    This robustness is achieved by only using limited "pooling" of information across observations. 
    For more details, see the paper by Lee and Weidner (2021), "Bounding Treatment Effects by Pooling Limited Information across Observations," <arXiv:2111.05243>.

Addressing measurement error in covariates and misclassification in binary outcome variables within causal inference, the 'ATE.ERROR' package implements inverse probability weighted estimation methods proposed by Shu and Yi (2017, <doi:10.1177/0962280217743777>; 2019, <doi:10.1002/sim.8073>). These methods correct errors to accurately estimate average treatment effects (ATE). The package includes two main functions: ATE.ERROR.Y() for handling misclassification in the outcome variable and ATE.ERROR.XY() for correcting both outcome misclassification and covariate measurement error. It employs logistic regression for treatment assignment and uses bootstrap sampling to calculate standard errors and confidence intervals, with simulated datasets provided for practical demonstration.

A recent method proposed by Yi and Chen (2023) <doi:10.1177/09622802221146308> is used to estimate the average treatment effects using noisy data containing both measurement error and spurious variables. The package 'AteMeVs' contains a set of functions that provide a step-by-step estimation procedure, including the correction of the measurement error effects, variable selection for building the model used to estimate the propensity scores, and estimation of the average treatment effects. The functions contain multiple options for users to implement, including different ways to correct for the measurement error effects, distinct choices of penalty functions to do variable selection, and various regression models to characterize propensity scores.