Maximum likelihood estimation, random values generation, density computation and other functions for the exponential-Poisson generalised exponential-Poisson and Poisson-exponential distributions. References include: Rodrigues G. C., Louzada F. and Ramos P. L. (2018). "Poisson-exponential distribution: different methods of estimation". Journal of Applied Statistics, 45(1): 128--144. <doi:10.1080/02664763.2016.1268571>. Louzada F., Ramos, P. L. and Ferreira, H. P. (2020). "Exponential-Poisson distribution: estimation and applications to rainfall and aircraft data with zero occurrence". Communications in Statistics--Simulation and Computation, 49(4): 1024--1043. <doi:10.1080/03610918.2018.1491988>. Barreto-Souza W. and Cribari-Neto F. (2009). "A generalization of the exponential-Poisson distribution". Statistics and Probability Letters, 79(24): 2493--2500. <doi:10.1016/j.spl.2009.09.003>.

Implements a new multiple imputation method that draws
    imputations from a latent joint multivariate normal model which
    underpins generally structured data. This model is constructed using a
    sequence of flexible conditional linear models that enables the
    resulting procedure to be efficiently implemented on high dimensional
    datasets in practice. See Robbins (2021) <arXiv:2008.02243>.

Provides tools to download comprehensive datasets of local,
    state, and federal election results in Germany from 1990 to 2025. The package
    facilitates access to data on turnout, vote shares for major parties, and
    demographic information across different levels of government (municipal, state,
    and federal). It offers access to geographically harmonized datasets
    that account for changes in municipal boundaries over time and incorporate
    mail-in voting districts. Includes bundled county-level covariates from INKAR
    and municipality-level Census 2022 data.
    Users can easily retrieve, clean, and standardize German electoral data,
    making it ready for analysis. Data is sourced from
    <https://github.com/awiedem/german_election_data>.