formatting_fits_agg_dm.md
In dRiftDM: Estimating (Time-Dependent) Drift Diffusion Models

print.fits_agg_dm prints header via summary and returns invisibly

Code
  print(fits_agg)
Output
  Fit approach: aggregated - classical
  Fitted model type: ratcliff_dm, drift_dm
  Optimizer: nmkb
  Convergence: TRUE
  N Individuals: 3 
  Average Trial Numbers:
   100 trials null

print.summary.fits_agg_dm prints header only when requested

Code
  print(sum_obj, just_header = TRUE)
Output
  Fit approach: aggregated - classical
  Fitted model type: ratcliff_dm, drift_dm
  Optimizer: nmkb
  Convergence: TRUE
  N Individuals: 3 
  Average Trial Numbers:
   100 trials null

Code
  print(sum_obj)
Output
  Fit approach: aggregated - classical
  Fitted model type: ratcliff_dm, drift_dm
  Optimizer: nmkb
  Convergence: TRUE
  N Individuals: 3 
  Average Trial Numbers:
   100 trials null

  Parameters:
         muc     b non_dec
  null 3.343 0.549   0.341

  Cost Function: rmse

  Fit Indices:
      Log_Like Neg_Log_Like          AIC          BIC       RMSE_s      RMSE_ms 
            NA           NA           NA           NA        0.011       10.744

  -------
  Deriving PDFS:
    solver: kfe
    values: sigma=1, t_max=3, dt=0.0075, dx=0.02, nt=400, nx=100

Code
  print(sum_obj, round_digits = 2)
Output
  Fit approach: aggregated - classical
  Fitted model type: ratcliff_dm, drift_dm
  Optimizer: nmkb
  Convergence: TRUE
  N Individuals: 3 
  Average Trial Numbers:
   100 trials null

  Parameters:
        muc    b non_dec
  null 3.34 0.55    0.34

  Cost Function: rmse

  Fit Indices:
      Log_Like Neg_Log_Like          AIC          BIC       RMSE_s      RMSE_ms 
            NA           NA           NA           NA         0.01        10.74

  -------
  Deriving PDFS:
    solver: kfe
    values: sigma=1, t_max=3, dt=0.0075, dx=0.02, nt=400, nx=100