Detect feedback loops (cycles, circuits) between species (nodes) in ordinary differential equation (ODE) models. Feedback loops are paths from a node to itself without visiting any other node twice, and they have important regulatory functions. Loops are reported with their order of participating nodes and their length, and whether the loop is a positive or a negative feedback loop. An upper limit of the number of feedback loops limits runtime (which scales with feedback loop count). Model parametrizations and values of the modelled variables are accounted for. Computation uses the characteristics of the Jacobian matrix as described e.g. in Thomas and Kaufman (2002) <doi:10.1016/s1631-0691(02)01452-x>. Input can be the Jacobian matrix of the ODE model or the ODE function definition; in the latter case, the Jacobian matrix is determined using 'numDeriv'. Graph-based algorithms from 'igraph' are employed for path detection.

Performs extreme value analysis at multiple locations using functions from the 'evd' package. Supports both point-based and gridded input data using the 'terra' package, enabling flexible looping across spatial datasets for batch processing of generalised extreme value, Gumbel fits.

Common coordinate-based workflows involving processed chromatin loop and genomic element data are considered and packaged into appropriate customizable functions. Includes methods for linking element sets via chromatin loops and creating consensus loop datasets.

Change-point detection algorithm with
 label constraints and a penalty for each change outside of labels.
 Read TD Hocking, A Srivastava (2023) <doi:10.1007/s00180-022-01238-z>
 for details.

Estimates marginal likelihood from a posterior sample using the method described in Wang et al. (2023) <doi:10.1093/sysbio/syad007>, which does not require evaluation of any additional points and requires only the log of the unnormalized posterior density for each sampled parameter vector.

Extracts decision rules from tree- and rule-based models
    fitted in 'R'. Rules are expressed as logical predicates that identify
    paths to terminal nodes, making model behavior more transparent and
    interpretable. Provides conversion methods to 'partykit' party objects
    for a wide range of model types. The 'partykit' infrastructure is 
    described in Hothorn and Zeileis (2015) 
    <https://jmlr.org/papers/v16/hothorn15a.html>.