A system provides a set of functions for
    working with data from the Federal Deposit Insurance Corporation
    (FDIC), including retrieving financial data for FDIC-insured
    institutions and accessing the data taxonomy.

Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".

Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) <doi:10.1198/TECH.2009.08104>.

Forest data quality is a package containing nine methods of analysis for forest databases,
    from databases containing inventory data and growth models, the focus of the analyzes is related to the
    quality of the data present in the database with a focus on consistency , punctuality and completeness of data.

FDR functions for permutation-based estimators, including pi0 as well as FDR
             confidence intervals. The confidence intervals account for dependencies between
             tests by the incorporation of an overdispersion parameter, which is estimated
             from the permuted data. Also included are options for an analog parametric approach.