Computes bounds and sensitivity parameters as part of sensitivity 
    analysis for selection bias. Different bounds are provided: the SV (Smith 
    and VanderWeele), AF (assumption-free) bound, GAF (generalized 
    AF), and CAF (counterfactual AF) bounds. The calculation of the sensitivity 
    parameters for the SV and GAF bounds assume an additional dependence 
    structure in form of a generalized M-structure. The bounds can be
    calculated for any structure as long as the necessary assumptions hold. See 
    Smith and VanderWeele (2019) <doi:10.1097/EDE.0000000000001032>, 
    Zetterstrom and Waernbaum (2022) <doi:10.1515/em-2022-0108> and
    Zetterstrom (2024) <doi:10.1515/em-2023-0033>.

Multi-stage selection is practiced in numerous fields of life
    and social sciences and particularly in breeding. A special characteristic of
    multi-stage selection is that candidates are evaluated in successive stages
    with increasing intensity and effort, and only a fraction of the superior
    candidates is selected and promoted to the next stage. For the optimum design
    of such selection programs, the selection gain plays a crucial role. It can be
    calculated by integration of a truncated multivariate normal (MVN) distribution.
    While mathematical formulas for calculating the selection gain and the variance
    among selected candidates were developed long time ago, solutions for numerical
    calculation were not available. This package can also be used for optimizing
    multi-stage selection programs for a given total budget and different costs of
    evaluating the candidates in each stage.

New tools for post-selection inference, for use with forward
    stepwise regression, least angle regression, the lasso, and the many means
    problem. The lasso function implements Gaussian, logistic and Cox survival
    models.

Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).