Implements IV-estimator and Bayesian estimator for linear-in-means Spatial Autoregressive (SAR) model (see LeSage, 1997 <doi:10.1177/016001769702000107>; Lee, 2004 <doi:10.1111/j.1468-0262.2004.00558.x>; Bramoullé et al., 2009 <doi:10.1016/j.jeconom.2008.12.021>), while assuming that only a partial information about the network structure is available. Examples are when the adjacency matrix is not fully observed or when only consistent estimation of the network formation model is available (see Boucher and Houndetoungan <https://ahoundetoungan.com/files/Papers/PartialNetwork.pdf>).

A toolbox to create a particle swarm optimisation (PSO), the package contains two classes: the Particle and the Particle Swarm, this two class is used to run the PSO with methods to easily print, plot and save the result.

Simulating particle movement in 2D space has many application. The
    'particles' package implements a particle simulator based on the ideas 
    behind the 'd3-force' 'JavaScript' library. 'particles' implements all 
    forces defined in 'd3-force' as well as others such as vector fields, traps, 
    and attractors.

Producing the time-dependent receiver operating characteristic (ROC) curve through parametric approaches. Tools for
    generating random data, fitting, predicting and check goodness of fit are prepared. The methods are
    developed from the theoretical framework of proportional hazard model and copula functions.
    Using this package, users can now simulate parametric time-dependent ROC and run experiment
    to understand the behavior of the curve under different scenario.

A fast and flexible framework for agglomerative partitioning.
    'partition' uses an approach called Direct-Measure-Reduce to create
    new variables that maintain the user-specified minimum level of
    information. Each reduced variable is also interpretable: the original
    variables map to one and only one variable in the reduced data set.
    'partition' is flexible, as well: how variables are selected to
    reduce, how information loss is measured, and the way data is reduced
    can all be customized.  'partition' is based on the Partition
    framework discussed in Millstein et al. (2020) 
    <doi:10.1093/bioinformatics/btz661>.