The straightforward filtering index (SFINX) identifies true positive
    protein interactions in a fast, user-friendly, and highly accurate way.
    It is not only useful for the filtering of affinity purification -
    mass spectrometry (AP-MS) data, but also for similar types of data
    resulting from other co-complex interactomics technologies, such as TAP-MS,
    Virotrap and BioID. SFINX can also be used via the website interface at
    <http://sfinx.ugent.be>.

Helpers for addressing the issue of disconnected spatial units. 
    It allows for convenient adding and removal of neighbourhood connectivity between areal units prior to modelling, with the visual aid of maps.
    Post-modelling, it reduces the human workload for extracting, tidying and mapping predictions from areal models.

Generates Skew Factor Models data and applies Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Projected Principal Component (PPC), Perturbation Principal Component (PPC), Stochastic Approximation Principal Component (SAPC), Sparse Principal Component (SPC) and other PC methods to estimate model parameters. It includes capabilities for calculating mean squared error, relative error, and sparsity of the loading matrix.The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

Provides a tidy approach to spatial network
    analysis, in the form of classes and functions that enable a seamless
    interaction between the network analysis package 'tidygraph' and the
    spatial analysis package 'sf'.

Provides monthly statistics on the number of monthly air passengers at SFO airport such as operating airline, terminal, geo, etc.
             Data source: San Francisco data portal (DataSF) <https://data.sfgov.org/Transportation/Air-Traffic-Passenger-Statistics/rkru-6vcg>.

We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. The primary objective of the package is to generate OCDs using four methods viz., M array method, Juxtapose method, Orthogonal Integer Array and Hadamard method. The package also evaluates space filling properties of both the base design and OCDs using the MaxPro criterion, providing a meaningful basis for comparison. In addition, it includes tool to visualize the spread offered by the design points in the form of scatterplot, which help users to assess distribution and coverage of design points.