Learning a mixed directed acyclic graph based on both continuous and categorical data.

Projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics. S. Kucheryavskiy (2020) <doi:10.1016/j.chemolab.2020.103937>.

Provides 3D plots of the Moran-Downton bivariate exponential distribution (MDBED), 
  generates bivariate random values, and also provides values of the joint and conditional PDFs and CDFs. 
  Nagao M, Kadoya M (1971) <http://hdl.handle.net/2433/124795>.
  Balakrishna N, Lai CD (2009) <doi:10.1007/b101765>.

Use the open source 'MDB Tools' utilities
    <https://github.com/mdbtools/mdbtools/>. Primarily used for converting
    proprietary Microsoft Access files to simple text files and then
    reading those as data frames.

Correct identification and handling of missing data is one of the most important steps in any analysis. To aid this process, 'mde' provides a very easy to use yet robust framework to quickly get an idea of where the missing data
             lies and therefore find the most appropriate action to take.
             Graham WJ (2009) <doi:10.1146/annurev.psych.58.110405.085530>.

Provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.

Causal and statistical inference on an arbitrary treatment
    effect curve requires care in both estimation and inference.  This
    package, implements the Method of Direct Estimation and Inference as introduced in "Estimation and Inference on Nonlinear and Heterogeneous Effects" by Ratkovic and Tingley (2023) <doi:10.1086/723811>.  The method takes an outcome, variable of theoretical interest
    (treatment), and set of variables and then returns a partial
    derivative (marginal effect) of the treatment variable at each point
    along with uncertainty intervals. The approach offers two advances.
    First, a split-sample approach is used as a guard against over-fitting.
    Second, the method uses a data-driven interval derived from conformal
    inference, rather than relying on a normality assumption on the error
    terms.