Multiscale Curvature Classification of ground returns in 3-D LiDAR 
    point clouds, designed for forested environments. 'RMCC' is a porting to R of the 
    'MCC-lidar' method by Evans and Hudak (2007) <doi:10.1109/TGRS.2006.890412>.

Provides different methods of multi-criteria decision analysis.

MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M. Draminski, J. Koronacki (2018) <doi:10.18637/jss.v085.i12>.

Collection of methods for rating matrix completion, which is a statistical framework for recommender systems. Another relevant application is the imputation of rating-scale survey data in the social and behavioral sciences. Note that matrix completion and imputation are synonymous terms used in different streams of the literature. The main functionality implements robust matrix completion for discrete rating-scale data with a low-rank constraint on a latent continuous matrix (Archimbaud, Alfons, and Wilms (2025) <doi:10.48550/arXiv.2412.20802>). In addition, the package provides wrapper functions for 'softImpute' (Mazumder, Hastie, and Tibshirani, 2010, <https://www.jmlr.org/papers/v11/mazumder10a.html>; Hastie, Mazumder, Lee, Zadeh, 2015, <https://www.jmlr.org/papers/v16/hastie15a.html>) for easy tuning of the regularization parameter, as well as benchmark methods such as median imputation and mode imputation.

Functions for simulating Markov chains using the Barker proposal
    to compute Markov chain Monte Carlo (MCMC) estimates of expectations with
    respect to a target distribution on a real-valued vector space. The Barker
    proposal, described in Livingstone and Zanella (2022)
    <doi:10.1111/rssb.12482>, is a gradient-based MCMC algorithm inspired by the
    Barker accept-reject rule. It combines the robustness of simpler MCMC
    schemes, such as random-walk Metropolis, with the efficiency of
    gradient-based methods, such as the Metropolis adjusted Langevin algorithm. 
    The key function provided by the package is sample_chain(), which allows
    sampling a Markov chain with a specified target distribution as its
    stationary distribution. The chain is sampled by generating proposals and
    accepting or rejecting them using a Metropolis-Hasting acceptance rule.
    During an initial warm-up stage, the parameters of the proposal distribution
    can be adapted, with adapters available to both: tune the scale of the
    proposals by coercing the average acceptance rate to a target value; tune
    the shape of the proposals to match covariance estimates under the target 
    distribution. As well as the default Barker proposal, the package also
    provides implementations of alternative proposal distributions, such as
    (Gaussian) random walk and Langevin proposals. Optionally, if 'BridgeStan's
    R interface <https://roualdes.us/bridgestan/latest/languages/r.html>,
    available on GitHub <https://github.com/roualdes/bridgestan>, is installed,
    then 'BridgeStan' can be used to specify the target distribution to sample
    from.