An implementation of the Rapid Assessment Method for Older People
    or RAM-OP <https://www.helpage.org/resource/rapid-assessment-method-for-older-people-ramop-manual/>.
    It provides various functions that allow the user to design and plan the
    assessment and analyse the collected data. RAM-OP provides accurate and
    reliable estimates of the needs of older people.

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 olr function systematically evaluates multiple linear regression models by exhaustively fitting all possible combinations of independent variables against the specified dependent variable. It selects the model that yields the highest adjusted R-squared (by default) or R-squared, depending on user preference. In model evaluation, both R-squared and adjusted R-squared are key metrics: R-squared measures the proportion of variance explained but tends to increase with the addition of predictors—regardless of relevance—potentially leading to overfitting. Adjusted R-squared compensates for this by penalizing model complexity, providing a more balanced view of fit quality. The goal of olr is to identify the most suitable model that captures the underlying structure of the data while avoiding unnecessary complexity. By comparing both metrics, it offers a robust evaluation framework that balances predictive power with model parsimony. Example Analogy: Imagine a gardener trying to understand what influences plant growth (the dependent variable). They might consider variables like sunlight, watering frequency, soil type, and nutrients (independent variables). Instead of manually guessing which combination works best, the olr function automatically tests every possible combination of predictors and identifies the most effective model—based on either the highest R-squared or adjusted R-squared value. This saves the user from trial-and-error modeling and highlights only the most meaningful variables for explaining the outcome. A Python version is also available at <https://pypi.org/project/olr>.