Implements convex regression with interpretable sharp partitions
    (CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 <http://jmlr.org/papers/volume17/15-344/15-344.pdf>.

Designs guide sequences for CRISPR/Cas9 genome editing and 
    provides information on sequence features pertinent to guide 
    efficiency. Sequence features include annotated off-target 
    predictions in a user-selected genome and a predicted efficiency 
    score based on the model described in Doench et al. (2016) 
    <doi:10.1038/nbt.3437>. Users are able to import additional genomes 
    and genome annotation files to use when searching and annotating 
    off-target hits. All guide sequences and off-target data can be 
    generated through the 'R' console with sgRNA_Design() or through 
    'crispRdesignR's' user interface with crispRdesignRUI(). CRISPR 
    (Clustered Regularly Interspaced Short Palindromic Repeats) and the 
    associated protein Cas9 refer to a technique used in genome editing.

A collection of functions for modeling fissile material operations in nuclear facilities,
    based on Zywiec et al (2021) <doi:10.1016/j.ress.2020.107322>.

An R implementation of the Critical Path Method (CPM).
    CPM is a method used to estimate the minimum project duration and determine 
    the amount of scheduling flexibility on the logical network paths within the 
    schedule model. The flexibility is in terms of early start, early finish, 
    late start, late finish, total float and free float. Beside, it permits 
    to quantify the complexity of network diagram through the analysis of 
    topological indicators. Finally, it permits to change the activities duration 
    to perform what-if scenario analysis. The package was built based on following 
    references: To make topological sorting and other graph operation, we use 
    Csardi, G. & Nepusz, T. (2005) 
    <https://www.researchgate.net/publication/221995787_The_Igraph_Software_Package_for_Complex_Network_Research>;
    For schedule concept, the reference was Project Management Institute (2017) 
    <https://www.pmi.org/pmbok-guide-standards/foundational/pmbok>;
    For standards terms, we use Project Management Institute (2017) 
    <https://www.pmi.org/pmbok-guide-standards/lexicon>;
    For algorithms on Critical Path Method development, we use 
    Vanhoucke, M. (2013) <doi:10.1007/978-3-642-40438-2> and 
    Vanhoucke, M. (2014) <doi:10.1007/978-3-319-04331-9>; 
    And, finally, for topological definitions, we use
    Vanhoucke, M. (2009) <doi:10.1007/978-1-4419-1014-1>.