Estimation of an S-shaped function and its corresponding inflection point via a least squares approach. A sequential mixed primal-dual based algorithm is implemented for the fast computation. Details can be found in Feng et al. (2022) <doi:10.1111/rssb.12481>.

Spatial stratified heterogeneity (SSH) denotes the coexistence of within-strata homogeneity and between-strata heterogeneity. Information consistency-based methods provide a rigorous approach to quantify SSH and evaluate its role in spatial processes, grounded in principles of geographical stratification and information theory (Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>; Wang, J. et al. (2024) <doi:10.1080/24694452.2023.2289982>).

Convenient tools for exchanging files securely from within R. By
    encrypting the content safe passage of files (shipment) can be provided by
    common but insecure carriers such as ftp and email. Based on asymmetric
    cryptography no management of shared secrets is needed to make a secure
    shipment as long as authentic public keys are available. Public keys used
    for secure shipments may also be obtained from external providers as part of
    the overall process. Transportation of files will require that relevant
    services such as ftp and email servers are available.

Implements the Shimazaki-Shinomoto method for optimizing the
    bin width of histograms and the bandwidth of kernel density estimators.
    The framework minimizes the expected Mean Integrated Squared Error (MISE)
    and supports both 1D and 2D distributions, fixed and locally adaptive
    estimators, bootstrap confidence intervals, and 'OpenMP'-accelerated 'C++' 'backends'. Ideally suited for time-dependent rate estimation and
    identifying intrinsic data structures. For more details see Shimazaki and
    Shinomoto (2007) <doi:10.1162/neco.2007.19.6.1503> and Shimazaki and
    Shinomoto (2010) <doi:10.1007/s10827-009-0180-4>.

Evaluating the consistency assumption of Network Meta-Analysis both globally and locally in the Bayesian framework. Inconsistencies are located by applying Bayesian variable selection to the inconsistency factors. The implementation of the method is described by Seitidis et al. (2023) <doi:10.1002/sim.9891>.