Optimal testing under general dependence. The R package implements procedures proposed in Wang, Han, and Tong (2022). The package includes parameter estimation procedures, the computation for the posterior probabilities, and the testing procedure.

Use optimization to estimate weights that balance covariates for binary, multinomial, and continuous treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>. The degree of balance can be specified for each covariate. In addition, sampling weights can be estimated that allow a sample to generalize to a population specified with given target moments of covariates.

Provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data.  The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift.  See <http://i.giwebb.com/index.php/research/association-discovery/> for more information in relation to the OPUS Miner algorithm.

Provides details such as Morphine Equivalent Dose (MED), 
    brand name and opioid content which are calculated of all oral opioids 
    authorized for sale by Health Canada and the FDA based on their Drug Identification Number (DIN) or National Drug Code (NDC). 
    MEDs are calculated based on recommendations by Canadian Institute for Health Information (CIHI) and Von Korff et al (2008)
    and information obtained from Health Canada's Drug Product Database's monthly data dump or FDA Daily database for Canadian and US databases respectively. 
    Please note in no way should output from this package be a substitute for medical advise.  
    All medications should only be consumed on prescription from a licensed healthcare provider.

Empower your data analysis with 'orbweaver', an R package
    designed for effortless construction and analysis of graph data
    structures. With 'orbweaver', you can seamlessly build and manipulate
    graph structures, leveraging its high-performance methods for
    filtering, joining, and mutating data within the R environment.
    Drawing inspiration from the efficiency of the 'data.table'
    package, 'orbweaver' ensures that mutations and changes to
    the graph are performed in place, streamlining your
    workflow for optimal productivity.