Contains a time series classification method that obtains a set of filters that maximize the between-class and minimize the within-class distances.

A wrapper around the new 'cleaner' package, that allows
  data cleaning functions for classes 'logical', 'factor', 'numeric', 
  'character', 'currency' and 'Date' to make data cleaning fast and
  easy. Relying on very few dependencies, it provides smart guessing,
  but with user options to override anything if needed.

Curates biological sequences massively, quickly, without errors and 
 without internet connection.  Biological sequences curing is performed by 
 aligning the forward and / or revers primers or ends of cloning vectors with the sequences 
 to be cleaned. After the alignment, new subsequences are generated without biological fragment 
 not desired by the user.
 Pozzi et al (2020) <doi:10.1007/s00438-020-01671-z>.

Wrapper of .Call() that runs exit handlers to clean up C
    resources. Helps managing C (non-R) resources while using the R API.

Functions to work with data frames to prepare data for further analysis.
    The functions for imputation, encoding, partitioning, and other manipulation can produce log files to keep track of process.

Cleaning and standardizing tabular data package, tailored
    specifically for curating epidemiological data. It streamlines various
    data cleaning tasks that are typically expected when working with
    datasets in epidemiology. It returns the processed data in the same
    format, and generates a comprehensive report detailing the outcomes of
    each cleaning task.

Data cleaning functions for classes logical,
  factor, numeric, character, currency and Date to make
  data cleaning fast and easy. Relying on very few dependencies, it 
  provides smart guessing, but with user options to override 
  anything if needed.

Automated method for doublet detection in flow or mass cytometry data, based on simulating doublets and finding events whose protein expression patterns are similar to the simulated doublets.