Point and interval estimation in dual frame surveys. In contrast
    to classic sampling theory, where only one sampling frame is considered,
    dual frame methodology assumes that there are two frames available for
    sampling and that, overall, they cover the entire target population. Then,
    two probability samples (one from each frame) are drawn and information
    collected is suitably combined to get estimators of the parameter of
    interest.

With no external dependencies and
    support for 335 languages; all languages spoken by
    more than one million speakers. 'Franc' is a port
    of the 'JavaScript' project of the same name,
    see <https://github.com/wooorm/franc>.

Accompanying package of the book 'Financial Risk Modelling
        and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.

Analysis of Fluorescence Recovery After Photobleaching (FRAP) experiments using nonlinear mixed-effects regression models and analysis of the results. 'FRApp' is not limited to the analysis of FRAP experiments only. Any nonlinear mixed-effects models with an asymptotic exponential functional relationship to hierarchical data in various domains can be fitted. 
  The analysis of data available in the package is presented in Di Credico, G., Pelucchi, S., Pauli, F. et al. (2025) <doi:10.1038/s41598-025-87154-w>.

Automatically process Fluorescence Recovery After Photobleaching (FRAP) data and generate consistent, publishable figures. Note: this package does not replace 'ImageJ' (or its equivalence) in raw image quantification. Some references about the methods: Sprague, Brian L. (2004) <doi:10.1529/biophysj.103.026765>; Day, Charles A. (2012) <doi:10.1002/0471142956.cy0219s62>.

Perform robust inference based on applying Fast and Robust Bootstrap 
    on robust estimators (Van Aelst and Willems (2013) <doi:10.18637/jss.v053.i03>). 
    This method constitutes an alternative to ordinary bootstrap or asymptotic inference. 
    procedures when using robust estimators such as S-, MM- or GS-estimators. 
    The available methods are multivariate regression, principal component analysis 
    and one-sample and two-sample Hotelling tests. It provides both the robust point 
    estimates and uncertainty measures based on the fast and robust bootstrap.