Collection of example animal sounds for bioacoustic analysis.

Implements the framework presented in Cucci, D. A., Voirol, L., Khaghani, M. and Guerrier, S. (2023) <doi:10.1109/TIM.2023.3267360> which allows to analyze the impact of sensor error modeling on the performance of integrated navigation (sensor fusion) based on inertial measurement unit (IMU), Global Positioning System (GPS), and barometer data.
             The framework relies on Monte Carlo simulations in which a Vanilla Extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms to recover a reference trajectory from noisy measurements. 
             The evaluation of several statistical metrics of the solution, aggregated over hundreds of simulated realizations, provides reasonable estimates of the expected performances of the system in real-world conditions.

Provides a navigation menu to enable pipe-friendly data processing for hierarchical data structures.
    By activating the menu items, you can perform operations on each item while maintaining the overall structure in attributes.

Implements the navigated weighting (NAWT) proposed by Katsumata (2020)
    <arXiv:2005.10998>, which improves the inverse probability weighting by 
    utilizing estimating equations suitable for a specific pre-specified parameter 
    of interest (e.g., the average treatment effects or the average treatment 
    effects on the treated) in propensity score estimation. It includes the 
    covariate balancing propensity score proposed by Imai and Ratkovic (2014) 
    <doi:10.1111/rssb.12027>, which uses covariate balancing conditions in 
    propensity score estimation. The point estimate of the parameter of interest
    as well as coefficients for propensity score estimation and their 
    uncertainty are produced using the M-estimation. The same functions can be 
    used to estimate average outcomes in missing outcome cases.

Model-based clustering of high-dimensional non-negative
             data that follow Generalized Negative Binomial distribution. All functions 
             in this package applies to either continuous or integer data. Correlation
            between variables are allowed, while samples are assumed to be independent.