Detect sample-provenance inconsistencies and potential mix-ups in mass-spectrometry-based
    plasma-proteome cohorts. Provides a clustering-based approach (build a
    nearest-neighbour graph in a dimensionality-reduced space and iteratively
    split large components by edge weight), a threshold-based approach
    (classify sample pairs as belonging or not-belonging from a pairwise
    distance cutoff), parameter optimization over distance metrics and
    cutoffs, and a pairwise random-forest classifier for protein importance
    ranking. This is a native R port of the author's Python package
    'spqrp' (<https://github.com/fhradilak/spqrp>), implementing methods from an associated manuscript currently in preparation.

Sparse arrays interpreted as multivariate polynomials.
    Uses 'disordR' discipline (Hankin, 2022,
    <doi:10.48550/ARXIV.2210.03856>).  To cite the package in
    publications please use Hankin (2022) <doi:10.48550/ARXIV.2210.10848>.

The notion of spreading activation is a prevalent metaphor in the cognitive sciences. This package provides the tools for cognitive scientists and psychologists to conduct computer simulations that implement spreading activation in a network representation. The algorithmic method implemented in 'spreadr' subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (2011, Frontiers), who viewed activation as a fixed cognitive resource that could spread among nodes that were connected to each other via edges or connections (i.e., a network). See Vitevitch, M. S., Ercal, G., & Adagarla, B. (2011). Simulating retrieval from a highly clustered network: Implications for spoken word recognition. Frontiers in Psychology, 2, 369. <doi:10.3389/fpsyg.2011.00369> and Siew, C. S. Q. (2019). spreadr: A R package to simulate spreading activation in a network. Behavior Research Methods, 51, 910-929. <doi: 10.3758/s13428-018-1186-5>.

The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.