A collection of functions to facilitate analysis of proteomic
    data from Olink, primarily NPX data that has been exported from Olink
    Software. The functions also work on QUANT data from
    Olink by log- transforming the QUANT data. The functions are focused
    on reading data, facilitating data wrangling and quality control
    analysis, performing statistical analysis and generating figures to
    visualize the results of the statistical analysis. The goal of this
    package is to help users extract biological insights from proteomic
    data run on the Olink platform.

An interface to easily run local language models with 'Ollama' <https://ollama.com> server
    and API endpoints (see <https://github.com/ollama/ollama/blob/main/docs/api.md> for details). It lets 
    you run open-source large language models locally on your machine.

Computes the pdf, cdf, quantile function, hazard function and generating random numbers for Odd log-logistic family (OLL-G). This family have been developed by different authors in the recent years. See Alizadeh (2019) <doi:10.31801/cfsuasmas.542988> for example.

Density, distribution function, quantile function and random generation for the Odd Log-Logistic Generalized Gamma proposed in Prataviera, F. et al (2017) <doi:10.1080/00949655.2016.1238088>.

The optimal linear regression olr(), runs all the possible combinations of linear regression
  equations. The olr() returns the equation which has the greatest adjusted R-squared term or the greatest R-squared term based
  on the user's discretion. Essentially, the olr() returns the best fit equation out of all the possible equations. R-squared increases 
  with the addition of an explanatory variable whether it is 'significant' or not, thus this was developed to eliminate that conundrum. 
  Adjusted R-squared is preferred to overcome this phenomenon, but each combination will still produce different results and this will
  return the best one. Complimentary functions are included which list all of the equations, all of the equations in ascending order, 
  a function to give the user a specific model's summary, and the list of adjusted R-squared terms & R-squared terms.
  A 'Python' version is available at: <https://pypi.org/project/olr/>.