The models of probability density functions are Gaussian or
    exponential distributions with polynomial correction terms.
    Using a maximum likelihood method, 'dsdp' computes parameters of Gaussian
    or exponential distributions together with degrees of polynomials by
    a grid search, and coefficient of polynomials by a variant of semidefinite
    programming. It adopts Akaike Information Criterion for model selection.
    See a vignette for a tutorial and more on our 'Github' repository 
    <https://github.com/tsuchiya-lab/dsdp/>.

Applies dynamic structural equation models to time-series data
	with generic and simplified specification for simultaneous and lagged
	effects. Methods are described in Thorson et al. (2024)
	"Dynamic structural equation models synthesize ecosystem dynamics 
	constrained by ecological mechanisms."

Provides a distributed framework for simulating and estimating skew factor models under various skewed and heavy-tailed distributions. The methods support distributed data generation, aggregation of local estimators, and evaluation of estimation performance via mean squared error, relative error, and sparsity measures. The distributed principal component (PC) estimators implemented in the package include 'IPC' (Independent Principal Component),'PPC' (Project Principal Component), 'SPC' (Sparse Principal Component), and other related distributed PC methods. The methodological background follows Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

'DataSHIELD' is an infrastructure and series of R packages that 
    enables the remote and 'non-disclosive' analysis of sensitive research data. 
    This package defines the API that is to be implemented by 'DataSHIELD' compliant 
    data repositories.

Performs distance sampling simulations. 'dsims' repeatedly generates
    instances of a user defined population within a given survey region. It then 
    generates realisations of a survey design and simulates the detection process. 
    The data are then analysed so that the results can be compared for accuracy 
    and precision across all replications. This process allows users to optimise 
    survey designs for their specific set of survey conditions. The effects of 
    uncertainty in population distribution or parameters can be investigated
    under a number of simulations so that users can be confident that they have
    achieved a robust survey design before deploying vessels into the field. The
    distance sampling designs used in this package from 'dssd' are detailed in
    Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 
    978-0199225873). General distance sampling methods are detailed in Introduction 
    to Distance Sampling: Estimating Abundance of Biological Populations, Buckland 
    et. al. (2004, ISBN-13: 978-0198509271). Find out more about estimating 
    animal/plant abundance with distance sampling at <https://distancesampling.org/>.