Performs sensitivity analysis for the sharp null, attributable effects, and weak nulls in matched studies with continuous exposures and binary or continuous outcomes as described in Zhang, Small, Heng (2024) <doi:10.48550/arXiv.2401.06909> and Zhang, Heng (2024) <doi:10.48550/arXiv.2409.12848>. Two of the functions require installation of the 'Gurobi' optimizer. Please see <https://docs.gurobi.com/current/#refman/ins_the_r_package.html> for guidance.

Provides a parallel backend for the %dopar% function using
        the snow package of Tierney, Rossini, Li, and Sevcikova.

Constructs dynamic optimal shrinkage estimators for the weights of the global 
  minimum variance portfolio which are reconstructed at given reallocation 
  points as derived in Bodnar, Parolya, and Thorsén (2021) (<arXiv:2106.02131>).
  Two dynamic shrinkage estimators are available in this package. One using 
  overlapping samples while the other use nonoverlapping samples.

Estimates dose-response relations from summarized dose-response
    data and to combines them according to principles of (multivariate)
    random-effects models.

A small package containing helper utilities for creating functions
    for computing statistics.

Renders DOT diagram markup language in R and also provides the possibility to
    export the graphs in PostScript and SVG (Scalable Vector Graphics) formats.
    In addition, it supports literate programming packages such as 'knitr' and
    'rmarkdown'.

Provides .C64(), which is an enhanced version of .C()
    and .Fortran() from the foreign function interface. .C64() supports long
    vectors, arguments of type 64-bit integer, and provides a mechanism to
    avoid unnecessary copies of read-only and write-only arguments. This
    makes it a convenient and fast interface to C/C++ and Fortran code.

Load configuration from a '.env' file, that is
    in the current working directory, into environment variables.

Decorrelates a set of summary statistics (i.e., Z-scores or P-values per SNP) via Decorrelation by Orthogonal Transformation (DOT) approach and performs gene-set analyses by combining transformed statistic values; operations are performed with algorithms that rely only on the association summary results and the linkage disequilibrium (LD). For more details on DOT and its power, see Olga (2020) <doi:10.1371/journal.pcbi.1007819>.