Estimation and inference for conditional linear quantile regression models using a convolution smoothed approach. In the low-dimensional setting, efficient gradient-based methods are employed for fitting both a single model and a regression process over a quantile range. Normal-based and (multiplier) bootstrap confidence intervals for all slope coefficients are constructed. In high dimensions, the conquer method is complemented with flexible types of penalties (Lasso, elastic-net, group lasso, sparse group lasso, scad and mcp) to deal with complex low-dimensional structures.

Extends 'ACER ConQuest' through a family of functions
    designed to improve graphical outputs and help with advanced analysis
    (e.g., differential item functioning).  Allows R users to call 
    'ACER ConQuest' from within R and read 'ACER ConQuest' System Files
    (generated by the command `put` <https://conquestmanual.acer.org/s4-00.html#put>).
    Requires 'ACER ConQuest' version 5.39 or later. 
    A demonstration version can be downloaded from <https://shop.acer.org/acer-conquest-5.html>.

Convert text into synthesized speech and get a list of supported voices for a region. 
    Microsoft's 'Cognitive Services Text to Speech REST' API <https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/rest-text-to-speech?tabs=streaming>  
    supports neural text to speech voices, which support specific languages and dialects that are identified by locale.

Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) <doi:10.3389/fmicb.2023.1170391>.

Merging data from multiple sources is a relevant approach for
    comprehensively evaluating complex systems. However, the inherent problems
    encountered when analyzing single tables are amplified with the generation
    of multi-block datasets, and finding the relationships between data layers
    of increasing complexity constitutes a challenging task. For that purpose,
    a generic methodology is proposed by combining the strengths of established
    data analysis strategies, i.e. multi-block approaches and the Orthogonal
    Partial Least Squares (OPLS) framework to provide an efficient tool for the
    fusion of data obtained from multiple sources. The package enables quick
    and efficient implementation of the consensus OPLS model for any horizontal
    multi-block data structure (observation-based matching). Moreover, it
    offers an interesting range of metrics and graphics to help to determine
    the optimal number of components and check the validity of the model
    through permutation tests. Interpretation tools include scores and loadings
    plots, as well as Variable Importance in Projection (VIP), and performance
    coefficients such as R2, Q2 and DQ2 coefficients. J. Boccard and D.N.
    Rutledge (2013) <doi:10.1016/j.aca.2013.01.022>.