Estimates and plots effect estimates from models with all possible 
    combinations of a list of variables. It can be used for assessing treatment 
    effects in clinical trials or risk factors in bio-medical and epidemiological 
    research. Like Stata command 'confall' (Wang Z (2007) <doi:10.1177/1536867X0700700203> ), 
    'allestimates' calculates and stores all effect estimates, and plots them against p values or 
    Akaike information criterion (AIC) values. It currently has functions for linear 
    regression: all_lm(), logistic and Poisson regression: all_glm(), 
    and Cox proportional hazards regression: all_cox().

Provides a function to calculate multiple performance metrics for actual and predicted values. In total eight metrics will be calculated for particular actual and predicted series. Helps to describe a Statistical model's performance in predicting a data. Also helps to compare various models' performance. The metrics are Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Mean absolute Error (MAE), Mean absolute percentage error (MAPE), Mean Absolute Scaled Error (MASE), Nash-Sutcliffe Efficiency (NSE), Willmott’s Index (WI), and Legates and McCabe Index (LME). Among them, first five are expected to be lesser whereas, the last three are greater the better. More details can be found from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202> and Garai et al. (2024) <doi:10.1007/s11063-024-11552-w>.

Evaluates acute lymphoblastic leukemia maintenance therapy practice at patient and cohort level.

Implements several exact methods for allocating optimal sample sizes when
    designing stratified samples. These methods are discussed in Wright (2012)
    <doi:10.1080/00031305.2012.733679> and Wright (2017)
    <doi:10.1016/j.spl.2017.04.026>.

Streamline use of the 'All of Us' Researcher Workbench (<https://www.researchallofus.org/data-tools/workbench/>)with tools to extract and manipulate data from the 'All of Us' database. Increase interoperability with the Observational Health Data Science and Informatics ('OHDSI') tool stack by decreasing reliance of 'All of Us' tools and allowing for cohort creation via 'Atlas'. Improve reproducible and transparent research using 'All of Us'.