val <- prepare_largeraw_sample()

val_small <- val[seq_len(3000), ]

model_search_nonlinear <- buildLKTModel(
  data = val_small,
  allcomponents = "KC..Default.",
  allfeatures = c("logitdec", "recency"),
  forv = 0,
  bacv = 0,
  forward = TRUE,
  backward = FALSE,
  maxitv = 2,
  verbose = FALSE
)

trace_nonlinear <- model_search_nonlinear[[1]]
fit_nonlinear <- model_search_nonlinear[[2]]
last_trace <- trace_nonlinear[nrow(trace_nonlinear), ]

final_bic <- last_trace$params * log(length(fit_nonlinear$prediction)) -
  2 * fit_nonlinear$loglike
final_aic <- last_trace$params * 2 - 2 * fit_nonlinear$loglike

check_true("nonlinear search accepted at least one feature", nrow(trace_nonlinear) > 1)
check_true("nonlinear search returned final model", !is.null(fit_nonlinear$coefs))
check_true(
  "nonlinear final model uses fixed selected parameters",
  is.atomic(fit_nonlinear$optimizedpars)
)
check_close("nonlinear final BIC matches trace", final_bic, last_trace$BIC, tolerance = 5e-2)
check_close("nonlinear final AIC matches trace", final_aic, last_trace$AIC, tolerance = 5e-2)