Implements a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem in fitting a log-binomial model under linear inequality constraints.

Implements the Bayesian Synthetic Control method for causal inference
    in comparative case studies. This package provides tools for estimating
    treatment effects in settings with a single treated unit and multiple control
    units, allowing for uncertainty quantification and flexible modeling of
    time-varying effects. The methodology is based on the paper by Vives and
    Martinez (2022) <doi:10.48550/arXiv.2206.01779>.

The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. 
        The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid.
        The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid.
        The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid.
        The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid.
        Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) <doi:10.1016/S0198-9715(01)00009-6>, 
        Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) <doi:10.1080/13658816.2014.937718>.

We utilize the Bradley-Terry Model to estimate the abilities of teams using paired comparison data. For dynamic approximation of current rankings, we employ the Exponential Decayed Log-likelihood function, and we also apply the Lasso penalty for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) <doi:10.1214/12-AOAS581>.