Efficient finite difference method for valuing European and American multi-asset options.

Estimation routines for several classes of affine term structure of interest rates models. All the models are based on the single-country unspanned macroeconomic risk framework from Joslin, Priebsch, and Singleton (2014, JF) <doi:10.1111/jofi.12131>. Multicountry extensions such as the ones of Jotikasthira, Le, and Lundblad (2015, JFE) <doi:10.1016/j.jfineco.2014.09.004>, Candelon and Moura (2023, EM) <doi:10.1016/j.econmod.2023.106453>, and Candelon and Moura (Forthcoming, JFEC) <http://hdl.handle.net/2078.1/249985> are also available.

Computationally efficient functions to provide direct likelihood-based
    inference for partially-observed multivariate birth-death processes.  Such processes
    range from a simple Yule model to the complex susceptible-infectious-removed model
    in disease dynamics.  Efficient likelihood evaluation facilitates maximum likelihood
    estimation and Bayesian inference.

Quantify the causal effect of a binary exposure on a binary
    outcome with adjustment for multiple biases. The functions can
    simultaneously adjust for any combination of uncontrolled confounding,
    exposure/outcome misclassification, and selection bias.
    The underlying method generalizes the concept of combining inverse
    probability of selection weighting with predictive value weighting.
    Simultaneous multi-bias analysis can be used to enhance the validity
    and transparency of real-world evidence obtained from observational,
    longitudinal studies. Based on the work from Paul Brendel, Aracelis Torres,
    and Onyebuchi Arah (2023) <doi:10.1093/ije/dyad001>.

Meta-analyses can be compromised by studies' internal biases (e.g.,
  confounding in nonrandomized studies) as well as by publication bias. This
  package conducts sensitivity analyses for the joint effects of these biases
  (per Mathur (2022) <doi:10.31219/osf.io/u7vcb>). These sensitivity analyses 
  address two questions: (1) For a given severity of internal bias across
  studies and of publication bias, how much could the results change?; and
  (2) For a given severity of publication bias, how severe would internal bias
  have to be, hypothetically, to attenuate the results to the null or by a given
  amount?