Tools to construct (or add to) cell-type signature matrices using flow sorted or single cell samples and deconvolve bulk gene expression data.
    Useful for assessing the quality of single cell RNAseq experiments, estimating the accuracy of signature matrices, and determining cell-type spillover. 
    Please cite: Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells <doi:10.1371/journal.pone.0224693>.

Adaptive smoothing functions for estimating the blood
    oxygenation level dependent (BOLD) effect by using functional Magnetic
    Resonance Imaging (fMRI) data, based on adaptive Gauss Markov random
    fields, for real as well as simulated data. The implemented models make 
    use of efficient Markov Chain Monte Carlo methods. Implemented methods 
    are based on the research developed by A. Brezger, L. Fahrmeir, A. 
    Hennerfeind (2007) <https://www.jstor.org/stable/4626770>.

The functions defined in this program serve for implementing adaptive
    two-stage tests. Currently, four tests are included: Bauer and Koehne (1994),
    Lehmacher and Wassmer (1999), Vandemeulebroecke (2006), and the horizontal conditional
    error function. User-defined tests can also be implemented. Reference: Vandemeulebroecke,
    An investigation of two-stage tests, Statistica Sinica 2006.

A wrapper for 'ada-url', a 'WHATWG' compliant and fast URL parser written in modern 'C++'. Also contains auxiliary functions such as a public suffix extractor.

A number of functions to create and analyze factorial plans according to the Design of Experiments (DoE) approach, with the addition of some utility function to perform some statistical analyses. DoE approach follows the approach in "Design and Analysis of Experiments" by Douglas C. Montgomery (2019, ISBN:978-1-119-49244-3). The package also provides utilities used in the course "Analysis of Data and Statistics" at the University of Trento, Italy.

Implements the adaptive sampling procedure, a framework for both positive unlabeled learning and learning with class label noise. Yang, P., Ormerod, J., Liu, W., Ma, C., Zomaya, A., Yang, J. (2018) <doi:10.1109/TCYB.2018.2816984>.