A DBI-compatible interface to ODBC databases.

A database resource that is accessible through the Open Database Connectivity ('ODBC') API. This package uses the Resource 
  model, with URL "resolver" and "client", to dynamically discover and make accessible tables stored in a 'MS SQL Server' database.
  For more details see Marcon (2021) <doi:10.1371/journal.pcbi.1008880>.

Download data from Brazil's Origin Destination Surveys. The package covers both data from household travel surveys, dictionaries of variables, and the spatial geometries of surveys conducted in different years and across various urban areas in Brazil. For some cities, the package will include enhanced versions of the data sets with variables "harmonized" across different years.

Anomaly detection in dynamic, temporal networks. The package 
    'oddnet' uses a feature-based method to identify anomalies. First, it computes 
    many features for each network. Then it models the features using time series 
    methods. Using time series residuals it detects anomalies. This way, the 
    temporal dependencies are accounted for when identifying anomalies 
    (Kandanaarachchi, Hyndman 2022) <arXiv:2210.07407>.

Conversion between the most common odds types for sports betting.
    Hong Kong odds, US odds, Decimal odds, Indonesian odds, Malaysian odds, and raw
    Probability are covered in this package.

Computes odds ratios and 95% confidence intervals from a generalized linear model object. It also computes model significance with the chi-squared statistic and p-value and it computes model fit using a contingency table to determine the percent of observations for which the model correctly predicts the value of the outcome. Calculates model sensitivity and specificity.

A utility to quickly obtain clean and tidy sports
    odds from The Odds API <https://the-odds-api.com>.

Uses the outputs of a logistic regression model, from caret <https://CRAN.R-project.org/package=caret>, to build an odds plot.
    This allows for the rapid visualisation of odds plot ratios and works best with the outputs of CARET's GLM model class, by returning the final trained model.