Error(s) in re-building vignettes:
  ...
--- re-building ‘regfilter.Rmd’ using rmarkdown
regIPF                package:regfilter                R Documentation

_I_t_e_r_a_t_i_v_e _P_a_r_t_i_t_i_o_n_i_n_g _F_i_l_t_e_r _f_o_r _R_e_g_r_e_s_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Application of the regIPF noise filtering method in a regression
     dataset.

_U_s_a_g_e:

     ## Default S3 method:
     regIPF(x, y, t = 0.4, nfolds = 10, vote = FALSE, p = 0.01, s = 3, i = 0.5, ...)
     
     ## S3 method for class 'formula'
     regIPF(formula, data, ...)
     
_A_r_g_u_m_e_n_t_s:

       x: a data frame of input attributes.

       y: a double vector with the output regressand of each sample.

       t: a double in [0,1] with the _threshold_ used by regression
          noise filter (default: 0.2).

  nfolds: number of folds in which the dataset is split (default: 10).

    vote: a logical indicating if the consensus voting ('TRUE') or
          majority voting ('FALSE') is used (default: 'FALSE').

       p: a double in [0,1] with the minimum proportion of original
          samples that must be labeled as noisy (default: 0.4).

       s: an integer with the number of iterations without improvement
          for the stopping criterion (default: 3).

       i: a double in [0,1] with the proportion of good samples which
          must be retained per iteration (default: 0.5).

     ...: other options to pass to the function.

 formula: a formula with the output regressand and, at least, one input
          attribute.

    data: a data frame in which to interpret the variables in the
          formula.

_D_e_t_a_i_l_s:

     In classification, _Iterative Partitioning Filter_ (IPF) builds a
     classifier with C4.5 on each fold ('nfolds') to evaluate the whole
     dataset. The noisy samples are removed depending on the chosen
     voting scheme (indicated by the argument 'vote'): if equal to
     'TRUE', a consensus voting is used (in which a sample is removed
     if it is misclassified by all the models); if equal to 'FALSE', a
     majority voting is used (in which a sample is removed if it is
     misclassified by more than a half of the models). In addition, IPF
     integrates an iterative process that stops depending on the
     arguments 'p', 's' and 'i'. The implementation of this noise
     filter to be used in regression problems follows the proposal of
     Martín _et al._ (2021), which is based on the use of a noise
     threshold ('t') to determine the similarity between the output
     variable of the samples.

_V_a_l_u_e:

     The result of applying the regression filter is a reduced dataset
     containing the clean samples (without errors or noise), since it
     removes noisy samples (those with errors). This function returns
     an object of class 'rfdata', which contains information related to
     the noise filtering process in the form of a list with the
     following elements:

  xclean: a data frame with the input attributes of clean samples
          (without errors).

  yclean: a double vector with the output regressand of clean samples
          (without errors).

numclean: an integer vector with the amount of clean samples.

 idclean: an integer vector with the indices of clean samples.

  xnoise: a data frame with the input attributes of noisy samples (with
          errors).

  ynoise: a double vector with the output regressand of noisy samples
          (with errors).

numnoise: an integer vector with the amount of noisy samples.

 idnoise: an integer vector with the indices of noisy samples.

  filter: the full name of the noise filter used.

   param: a list of the argument values.

    call: the function call.

     Note that objects of the class 'rfdata' support print.rfdata and
     summary.rfdata methods.

_R_e_f_e_r_e_n_c_e_s:

     T. M. Khoshgoftaar and P. Rebours, *Improving software quality
     prediction by noise filtering techniques*, _Journal of Computer
     Science and Technology_, 22:387-396, 2007. doi:
     10.1007/s11390-007-9054-2 (URL:
     https://doi.org/10.1007/s11390-007-9054-2)

     J. Martín, J. A. Sáez and E. Corchado, *On the regressand noise
     problem: Model robustness and synergy with regression-adapted
     noise filters.* _IEEE Access_, 9:145800-145816, 2021. doi:
     10.1109/ACCESS.2021.3123151 (URL:
     https://doi.org/10.1109/ACCESS.2021.3123151).

_S_e_e _A_l_s_o:

     'regIRF', 'regCVCF', 'regFMF', 'print.rfdata', 'summary.rfdata'

_E_x_a_m_p_l_e_s:

     # load the dataset
     data(rock)
     
     # usage of the default method
     set.seed(9)
     out.def <- regIPF(x = rock[,-ncol(rock)], y = rock[,ncol(rock)])
     
     # show results
     summary(out.def, showid = TRUE)
     
     # usage of the method for class formula
     set.seed(9)
     out.frm <- regIPF(formula = perm ~ ., data = rock)
     
     # check the match of noisy indices
     all(out.def$idnoise == out.frm$idnoise)
     

Quitting from lines 68-78 (regfilter.Rmd) 
Error: processing vignette ‘regfilter.Rmd’ failed with diagnostics:
DLL requires the use of native symbols
--- failed re-building ‘regfilter.Rmd’

SUMMARY: processing the following file failed:
  ‘regfilter.Rmd’

Error: Vignette re-building failed.
Execution halted