Implements two-stage pseudo maximum likelihood estimation (PMLE)
    for copula-based regression models with semi-competing risks data. The
    marginal distributions are modeled by semiparametric transformation
    regression models, and the dependence between bivariate event times is
    specified by a parametric copula function. See Arachchige, Chen and Zhou
    (2025) <doi:10.1007/s10985-024-09640-z> for details.

Given a sample with additive measurement error, the package estimates the deconvolution density - that is, the density of the underlying distribution of the sample without measurement error. The method maximises the log-likelihood of the estimated density, plus a quadratic smoothness penalty. The distribution of the measurement error can be either a known family, or can be estimated from a "pure error" sample. For known error distributions, the package supports Normal, Laplace or Beta distributed error. For unknown error distribution, a pure error sample independent from the data is used.

Estimate spatial autoregressive nonlinear probit models with and without autoregressive disturbances using partial maximum likelihood estimation. Estimation and inference regarding marginal effects is also possible. For more details see Bille and Leorato (2020) <doi:10.1080/07474938.2019.1682314>.

The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <https://dmg.org/>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products.

Provides a simple interface for extracting various elements from
  the publicly available PubMed XML files, incorporating PubMed's regular
  updates, and combining the data with the NIH Open Citation Collection. See
  Schoenbachler and Hughey (2021) <doi:10.7717/peerj.11071>.