Provides Sensory and Consumer Data 
    mapping and analysis <doi:10.14569/IJACSA.2017.081266>. The mapping visualization is made available
    from several features : options in dimension reduction methods and prediction models ranging from 
    linear to non linear regressions. A smoothed version of the map performed using locally weighted regression algorithm 
    is available. A selection process of map stability is provided. A 'shiny' application is included. 
    It presents an easy GUI for the implemented functions as well as a comparative tool of fit models
    using several criteria. Basic analysis such as characterization of products,
    panelists and sessions likewise consumer segmentation are also made available.

We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) <doi:10.1002/sim.7620> and Lindmark (2022) <doi:10.1007/s10260-021-00611-4>. The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships.

It allows to rapidly compute, bootstrap and plot up to fourth-order Sobol'-based sensitivity indices using several state-of-the-art first and total-order estimators. Sobol' indices can be computed either for models that yield a scalar as a model output or for systems of differential equations. The package also provides a suit of benchmark tests functions and several options to obtain publication-ready figures of the model output uncertainty and sensitivity-related analysis. An overview of the package can be found in Puy et al. (2022) <doi:10.18637/jss.v102.i05>.

Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008).

Interface to the 'Sensor Tower' API <https://app.sensortower.com/api/docs/app_analysis> 
    for mobile app analytics and market intelligence. Provides functions to retrieve 
    app metadata, publisher information, download and revenue estimates, active user 
    metrics, category rankings, and market trends. The package includes data processing 
    utilities to clean and aggregate metrics across platforms, automatic app name 
    resolution, and tools for generating professional analytics dashboards. Supports 
    both iOS and Android app ecosystems with unified data structures for cross-platform 
    analysis.