fit <- smoothic(
  formula = lcmedv ~ .,
  data = bostonhouseprice2,
  family = "sgnd", # Smooth Generalized Normal Distribution
  model = "mpr" # model location and scale
)

summary(fit)
#> Call:
#> smoothic(formula = lcmedv ~ ., data = bostonhouseprice2, family = "sgnd", 
#>     model = "mpr")
#> Family:
#> [1] "sgnd"
#> Model:
#> [1] "mpr"
#> 
#> Coefficients:
#> 
#> Location:
#>                     Estimate          SE       Z    Pvalue    
#> intercept_0_beta  3.61174584  0.08181155 44.1471 < 2.2e-16 ***
#> crim_1_beta      -0.02032255  0.00505227 -4.0225 7.082e-05 ***
#> zn_2_beta                  0           0       0         0    
#> indus_3_beta               0           0       0         0    
#> rm_4_beta         0.23357173  0.01171721 19.9341 < 2.2e-16 ***
#> age_5_beta       -0.00106083  0.00034141 -3.1072 0.0013172 ** 
#> rad_6_beta        0.00883974  0.00225510  3.9199 9.996e-05 ***
#> ptratio_7_beta   -0.02583598  0.00261809 -9.8683 3.944e-16 ***
#> lnox_8_beta      -0.28434660  0.08286384 -3.4315 0.0004889 ***
#> ldis_9_beta      -0.16025230  0.02294754 -6.9834 5.105e-10 ***
#> ltax_10_beta     -0.18420684  0.02140118 -8.6073 2.001e-13 ***
#> llstat_11_beta   -0.17153333  0.01837441 -9.3354 4.535e-15 ***
#> chast_12_beta     0.05015814  0.01969757  2.5464 0.0064919 ** 
#> 
#> Scale:
#>                    Estimate        SE       Z    Pvalue    
#> intercept_0_alpha -9.654035  2.288128 -4.2192 3.593e-05 ***
#> crim_1_alpha       0.019804  0.015717  1.2601 0.1328128    
#> zn_2_alpha                0         0       0         0    
#> indus_3_alpha     -0.032281  0.022037 -1.4648 0.0875849 .  
#> rm_4_alpha        -0.177752  0.102209 -1.7391 0.0482274 *  
#> age_5_alpha               0         0       0         0    
#> rad_6_alpha        0.033590  0.017613  1.9072 0.0327278 *  
#> ptratio_7_alpha           0         0       0         0    
#> lnox_8_alpha      -0.378692  0.818555 -0.4626 0.5116273    
#> ldis_9_alpha      -1.040374  0.269083 -3.8664 0.0001197 ***
#> ltax_10_alpha      1.354650  0.389785  3.4754 0.0004258 ***
#> llstat_11_alpha           0         0       0         0    
#> chast_12_alpha            0         0       0         0    
#> 
#> Shape:
#>                   Estimate      SE     Z   Pvalue   
#> intercept_0_nu     0.30119 0.10397 2.897 0.002437 **
#> 
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Kappa Estimate:
#> [1] 1.551471
#> Penalized Likelihood:
#> [1] 220.9246
#> IC Value:
#> [1] -441.8492

fit$kappa # shape estimate
#> [1] 1.551471

plot_paths(fit)

plot_effects(fit,
             what = c("ltax", "rm", "ldis"), # or "all" for all selected variables
             density_range = c(2.25, 3.75))