Generate synthetic station-based monthly climate time-series including
    temperature and rainfall, export to Network Common Data Form (NetCDF), 
    and provide visualization helpers for climate workflows. The approach is 
    inspired by statistical weather generator concepts described in Wilks (1999)
    <doi:10.1016/S0168-1923(99)00037-4> and Richardson (1981) <doi:10.1029/WR017i001p00182>.

Advanced climate simulation, forecasting, visualization, export,
    and machine learning tools. Generates synthetic climate datasets for single
    or multiple weather stations using stochastic weather generation techniques.
    'CDSimX' simulates daily climate variables including minimum and maximum
    temperature, rainfall, relative humidity, solar radiation, wind speed,
    wind direction, dew point temperature, and potential evapotranspiration.
    The package incorporates seasonal harmonic models, Markov chain rainfall
    occurrence processes, Gamma-distributed rainfall amounts, copula-based
    dependence structures, bias-correction procedures, and physical consistency
    constraints. 'CDSimX' supports climate data generation, environmental
    modeling, machine learning benchmarking, sensitivity analysis, and
    educational applications. Methods are based on established stochastic
    weather generation approaches described in Richardson (1981)
    <doi:10.1029/WR017i001p00182>, Wilks (1999)
    <doi:10.1016/S0168-1923(99)00037-4>, and Osei et al. (2026)
    <doi:10.5334/jors.666>.

Deriving skill structures from skill assignment 
    data for courses (sets of learning objects).

Bayesian component model-based network meta-analysis for treatment
    combinations with explicit component dose-response and dose-dependent
    interaction surfaces. The main interface fits single-timepoint arm-level
    networks with continuous or binary outcomes, any number of components,
    additive, bilinear, or saturating pairwise interactions, study-level random
    effects, and prediction at unobserved dose combinations. A stage-one
    longitudinal interface fits a two-component exponential time-course model
    with a bilinear interaction on the asymptote. Estimation uses 'Stan' through
    'cmdstanr' or 'rstan'. Methods are described in Welton et al. (2009)
    <doi:10.1093/aje/kwp014>, Mawdsley et al. (2016) <doi:10.1002/psp4.12091>,
    Wicha et al. (2017) <doi:10.1038/s41467-017-01929-y>, and Pedder et al.
    (2019) <doi:10.1002/jrsm.1351>.