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, ensuring
    seamless integration into existing workflows. Additionally, it 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.

Provides a set of utility tools to inspect spatial objects, facilitate
  handling and reporting of topology errors and geometry validity issue with sp objects.
  Finally, it provides a geometry cleaner that will fix all geometry problems,
  and eliminate (at least reduce) the likelihood of having issues when doing
  spatial data processing.

Provides essential Cleaning Validation functions for complying with pharmaceutical cleaning process regulatory standards. The package includes non-parametric methods to analyze drug active-ingredient residue (DAR), cleaning agent residue (CAR), and microbial colonies (Mic) for non-Poisson distributions. Additionally, Poisson methods are provided for Mic analysis when Mic data follow a Poisson distribution.