Implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.

Provides functions for working with primary
    event censored distributions and 'Stan' implementations for use in Bayesian
    modeling. Primary event censored distributions are useful for modeling
    delayed reporting scenarios in epidemiology and other fields (Charniga et
    al. (2024) <doi:10.48550/arXiv.2405.08841>). It also provides support for
    arbitrary delay distributions, a range of common primary distributions, and
    allows for truncation and secondary event censoring to be accounted for
    (Park et al. (2024) <doi:10.1101/2024.01.12.24301247>). A subset of
    common distributions also have analytical solutions implemented, allowing
    for faster computation. In addition, it provides multiple methods for
    fitting primary event censored distributions to data via optional
    dependencies.

Data from All the World's Primates relational SQL database and other tabular datasets are made available via drivers and connection functions. Additionally we provide several functions and examples to facilitate the merging and aggregation of these tabular inputs.

Use Prime Factorization for simplifying computations,
    for instance for ratios of large factorials.

Implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. <arXiv:1906.12125>.

Perform sample size, power calculation and subsequent analysis for Immuno-oncology (IO) trials composed of responders and non-responders.