Implementation of Nelson rules for control charts in 'R'. The 'Rspc' implements some Statistical Process Control methods, namely Levey-Jennings type of I (individuals) chart, Shewhart C (count) chart and Nelson rules (as described in Montgomery, D. C. (2013) Introduction to statistical quality control. Hoboken, NJ: Wiley.). Typical workflow is taking the time series, specify the control limits, and list of Nelson rules you want to evaluate. There are several options how to modify the rules (one sided limits, numerical parameters of rules, etc.). Package is also capable of calculating the control limits from the data (so far only for i-chart and c-chart are implemented).

Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) <doi:10.1080/10618600.2023.2231051> for the covariance-based method and Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537> for the operator-based rational approximation. These can be generated by the citation function in R.

R interface to the 'Spectra' library
    <https://spectralib.org/> for large-scale eigenvalue and SVD
    problems. It is typically used to compute a few
    eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues,
    which is usually more efficient than eigen() if k << n. This package
    provides the 'eigs()' function that does the similar job as in 'Matlab',
    'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function
    to calculate the largest k singular values and corresponding
    singular vectors of a real matrix. The matrix to be computed on can be
    dense, sparse, or in the form of an operator defined by the user.

Implements the network clustering algorithm described in 
            Newman (2006) <doi:10.1103/PhysRevE.74.036104>. The complete 
            iterative algorithm comprises of two steps. In the first step, the 
            network is expressed in terms of its leading eigenvalue and 
            eigenvector and recursively partition into two communities. 
            Partitioning occurs if the maximum positive 
            eigenvalue is greater than the tolerance (10e-5) for the current 
            partition, and if it results in a positive contribution to the 
            Modularity.
            Given an initial separation using the leading eigen step, 'rSpectral' 
            then continues to maximise for the change in Modularity using a 
            fine-tuning step - or variate thereof. The first stage here is to 
            find the node which, when moved from one community to another, 
            gives the maximum change in Modularity. This node’s community is 
            then fixed and we repeat the process until all nodes have been moved. 
            The whole process is repeated from this new state until the change 
            in the Modularity, between the new and old state, is less than the 
            predefined tolerance.
            A slight variant of the fine-tuning step, which can improve speed 
            of the calculation, is also provided. Instead of moving each node 
            into each community in turn, we only consider moves of neighbouring 
            nodes, found in different communities, to the community of the 
            current node of interest. The two steps process is repeatedly 
            applied to each new community found, subdivided each community 
            into two new communities, until we are unable to find any division 
            that results in a positive change in Modularity.