Specify, solve, and estimate dynamic stochastic general
    equilibrium (DSGE) models by maximum likelihood and Bayesian methods.
    Supports both linear models via an equation-based formula interface
    and nonlinear models via string-based equations with first-order
    perturbation (linearization around deterministic steady state).
    Solution uses the method of undetermined coefficients (Klein, 2000
    <doi:10.1016/S0165-1889(99)00045-7>). Likelihood evaluated via the
    Kalman filter. Bayesian estimation uses adaptive Random-Walk
    Metropolis-Hastings with prior specification. Additional tools include
    Kalman smoothing, historical shock decomposition, local identification
    diagnostics, parameter sensitivity analysis, second-order perturbation,
    occasionally binding constraints, impulse-response functions,
    forecasting, and robust standard errors.

Computes pathway-level transcriptional disruption scores
    from differential expression p-values using permutation tests with
    Generalized Pareto Distribution (GPD) tail extrapolation and false
    discovery rate (FDR) correction. Reference: Guo P, Li H (2026)
    DSGE: Disruption Score of Gene Expression for Gene-Set Enrichment
    Analysis. <https://github.com/LHJLab/DSGE>.

'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/>.