ApproxiW                ApproxiW
BLA_SARAR_ML            BLA_SARAR_ML BLA_SARAR_ML allows the estimation
                        of SARAR models using the gradient boosting
                        method with linear base learner for estimating
                        the coefficients Beta while the estimation of
                        the spatial parameter is based on a
                        concentrated likelihood function. This function
                        makes it possible to estimate a SARAR model
                        while automatically selecting the explanatory
                        variables.
BLA_SAR_ML              BLA_SAR_ML BLA_SAR_ML allows the estimation of
                        SAR models using the gradient boosting method
                        with linear base learner for estimating the
                        coefficients Beta while the estimation of the
                        spatial parameter is based on a concentrated
                        likelihood function. This function makes it
                        possible to estimate a SAR model while
                        automatically selecting the explanatory
                        variables.
BLA_SEM_ML              BLA_SEM_ML BLA_SEM_ML allows the estimation of
                        SEM models using the gradient boosting method
                        with linear base learner for estimating the
                        coefficients Beta while the estimation of the
                        spatial parameter is based on a concentrated
                        likelihood function. This function makes it
                        possible to estimate a SEM model while
                        automatically selecting the explanatory
                        variables.
BSPA_SARAR_CFE          BSPA_SARAR_CFE CFE-style alternating estimator
                        for SARAR models with a gamboost core.
BSPA_SARAR_ML           BSPA_SARAR_ML
BSPA_SAR_CFE            BSPA_SAR_CFE BSPA_SAR_CFE allows the estimation
                        of additive non linear SAR models using
                        gradient boosting for the non linear part while
                        the spatial parameter is estimated with the
                        determinant-free Closed-Form Estimator of
                        Smirnov (2020, doi:10.1111/gean.12268). This
                        function makes it possible to estimate an
                        additive non linear SAR model while
                        automatically selecting the explanatory
                        variables.
BSPA_SAR_ML             BSPA_SAR_ML BSPA_SAR_ML allows the estimation
                        of additive non linear SAR models using
                        gradient boosting for the non linear part while
                        the spatial parameter is estimated with a
                        concentrated likelihood function. This function
                        makes it possible to estimate an additive non
                        linear SAR model while automatically selecting
                        the explanatory variables.
BSPA_SEM_CFE            BSPA_SEM_CFE BSPA_SEM_CFE keeps the historical
                        SEM CFE interface while using the same one-shot
                        BRUT/filtered workflow as GAM_SEM_CFE: a
                        non-spatial BRUT CFE estimate is computed
                        first, then the filtered CFE backend is used
                        when the BRUT rho estimate is high.
BSPA_SEM_CFE_BRUT       BSPA_SEM_CFE_BRUT Experimental SEM CFE variant
                        using raw residuals for the CFE update.
BSPA_SEM_CFE_iter       BSPA_SEM_CFE_iter Iterative CFE estimator for
                        additive nonlinear SEM with joint updates of
                        spatial parameter and boosting fit.
BSPA_SEM_ML             BSPA_SEM_ML BSPA_SEM_ML allows the estimation
                        of additive non linear SAR models using the
                        gradient boosting method for estimating the non
                        linear part while the estimation of the spatial
                        parameter is based on a concentrated likelihood
                        function. This function makes it possible to
                        estimate an additive non linear SAR model while
                        automatically selecting the explanatory
                        variables.
GAM_SAR_CFE             GAM_SAR_CFE GAM_SAR_CFE allows the estimation
                        of additive non linear SAR models using
                        generalized additive models for the non linear
                        part while the spatial parameter is estimated
                        with the determinant-free Closed-Form Estimator
                        of Smirnov (2020, doi:10.1111/gean.12268). This
                        function makes it possible to estimate an
                        additive non linear SAR model while
                        automatically selecting the explanatory
                        variables.
GAM_SAR_ML              GAM_SAR_ML GAM_SAR_ML allows the estimation of
                        additive non linear SAR models using GAM/IPRLS
                        with thin plate regression spline (mgcv
                        package) for non linear part while the
                        estimation of the spatial parameter is based on
                        a concentrated likelihood function.
GAM_SEM_CFE             GAM_SEM_CFE GAM_SEM_CFE allows the estimation
                        of additive non linear SEM models using
                        generalized additive models for the non linear
                        part while the spatial parameter is estimated
                        with the determinant-free Closed-Form Estimator
                        of Smirnov (2020, doi:10.1111/gean.12268). This
                        function makes it possible to estimate an
                        additive non linear SEM model while
                        automatically selecting the explanatory
                        variables.
LM_SAR_ML               LM_SAR_ML LM_SAR_ML allows the estimation of
                        linear SAR model
MARS_SAR_CFE            MARS_SAR_CFE MARS_SAR_CFE estimates additive
                        nonlinear SAR models using a MARS backend
                        ('earth::earth') for the nonlinear component
                        and the determinant-free Closed-Form Estimator
                        of Smirnov (2020, doi:10.1111/gean.12268) for
                        the spatial autoregressive parameter.
MARS_SAR_ML             MARS_SAR_ML MARS_SAR_ML estimates additive
                        nonlinear SAR models using a MARS backend
                        ('earth::earth') for the nonlinear component
                        and concentrated likelihood for the spatial
                        autoregressive parameter.
MARS_SEM_CFE            MARS_SEM_CFE MARS_SEM_CFE estimates nonlinear
                        SEM models using a MARS backend
                        ('earth::earth') and the CFE approach for the
                        spatial error parameter.
MARS_SEM_ML             MARS_SEM_ML MARS_SEM_ML estimates nonlinear SEM
                        models using a MARS backend ('earth::earth')
                        and concentrated likelihood optimization for
                        the spatial error parameter.
SNR_SAR                 SNR_SAR
SNR_SEM                 SNR_SEM
XGBOOST_SAR_CFE         XGBOOST_SAR_CFE XGBOOST_SAR_CFE allows the
                        estimation of SAR models using the gradient
                        boosting method with linear base learner or
                        btree while the estimation of the spatial
                        parameter is based on the determinant-free
                        Closed-Form Estimator of Smirnov (2020,
                        doi:10.1111/gean.12268). This function makes it
                        possible to estimate a SAR linear or non linear
                        model while automatically selecting the
                        explanatory variables.
XGBOOST_SAR_ML          XGBOOST_SAR_ML XGBOOST_SAR_ML allows the
                        estimation of SAR models using the gradient
                        boosting method with linear base learner or
                        btree while the estimation of the spatial
                        parameter is based on a concentrated likelihood
                        function. This function makes it possible to
                        estimate a SAR linear or non linear model while
                        automatically selecting the explanatory
                        variables.
datatest                datatest is a simulated data for spatial
                        autoregressive non linear model
dgp                     dgp a function to simulate non-linear spatial
                        autoregressive SAR SEM and SARAR model.
fitted_decomp_spboost   fitted_decomp_spboost Decompose fitted values
                        of a spboost model by variable.
predict.spboost         Predict Method For 'spboost' Objects
predict_spboost         predict.spboost A prediction function for
                        object of class GAM_SAR_FIVA, GAM_SAR_ML,
                        BSPA_SAR_ML, MARS_SAR_ML, BLA_SAR_2SLS,
                        BLA_SAR_ML, BLA_SAR_2SLS,
                        XGBOOST_LINEAR_SAR_ML, XGBOOST_SAR_ML,
                        XGBOOST_LINEAR_SAR_CFE, XGBOOST_SAR_CFE. and
                        glmboost_sar.
spbgam                  spbgam spbgam allows the estimation of gaussian
                        additive non linear SAR/SEM models using
                        gradient boosting or generalized additive
                        models for estimating the non linear part of
                        the model while the estimation of the spatial
                        parameter is based on a concentrated likelihood
                        function (ML) or the determinant-free
                        Closed-Form Estimator of Smirnov (2020,
                        doi:10.1111/gean.12268). This function makes it
                        possible to estimate an additive non linear SAR
                        or SEM model while automatically selecting the
                        explanatory variables. If the functional forms
                        are already known, GAM ('mgcv') can be used
                        directly for the nonlinear component. When
                        variable selection or data-driven smoothness is
                        needed, gradient boosting ('mboost') is
                        preferred.
summary.spboost         Summary method for 'spboost' objects
