Fits the Bradley-Terry Model to Potentially Large and Sparse Networks of Comparison Data (BradleyTerryScalable)
Facilities are provided for fitting the simple, unstructured Bradley-Terry model to networks of binary comparisons. The implemented methods are designed to scale well to large, potentially sparse, networks. A fairly high degree of scalability is achieved through the use of EM and MM algorithms, which are relatively undemanding in terms of memory usage (relative to some other commonly used methods such as iterative weighted least squares, for example). Both maximum likelihood and Bayesian MAP estimation methods are implemented. The package provides various standard methods for a newly defined ‘btfit’ model class, such as the extraction and summarisation of model parameters and the simulation of new datasets from a fitted model. Tools are also provided for reshaping data into the newly defined ‘btdata’ class, and for analysing the comparison network, prior to fitting the Bradley-Terry model. This package complements, rather than replaces, the existing ‘BradleyTerry2’ package. (BradleyTerry2 has rather different aims, which are mainly the specification and fitting of ‘structured’ Bradley-Terry models in which the strength parameters depend on covariates.)

Quick Linear Regression (quickregression)
Helps to perform linear regression analysis by reducing manual effort. Reduces the independent variables based on specified p-value and Variance Inflation Factor (VIF) level.

AWS IAM Client Package (aws.iam)
A simple client for the Amazon Web Services (‘AWS’) Identity and Access Management (‘IAM’) ‘API’ <https://…/>.

Advanced Methods for Stochastic Frontier Analysis (sfadv)
Stochastic frontier analysis with advanced methods. In particular, it applies the approach proposed by Latruffe et al. (2017) <DOI:10.1093/ajae/aaw077> to estimate a stochastic frontier with technical inefficiency effects when one input is endogenous.

Bayesian Monotonic Regression Using a Marked Point Process Construction (monoreg)
An extended version of the nonparametric Bayesian monotonic regression procedure described in Saarela & Arjas (2011) <DOI:10.1111/j.1467-9469.2010.00716.x>, allowing for multiple additive monotonic components in the linear predictor, and time-to-event outcomes through case-base sampling. The extension and its applications, including estimation of absolute risks, are described in Saarela & Arjas (2015) <DOI:10.1111/sjos.12125>.