Sample size calculations in causal inference with observational data are increasingly desired. This package is a tool to calculate sample size under prespecified power with minimal summary quantities needed.

Provides quasi-Newton methods to minimize partially separable
    functions. The methods are largely described by  
    Nocedal and Wright (2006) <doi:10.1007/978-0-387-40065-5>.

Includes functions and data used in the book "Presenting Statistical Results Effectively", Andersen and Armstrong (2022, ISBN: 978-1446269800).  Several functions aid in data visualization - creating compact letter displays for simple slopes, kernel density estimates with normal density overlay.  Other functions aid in post-model evaluation heatmap fit statistics for binary predictors, several variable importance measures, compact letter displays and simple-slope calculation. Finally, the package makes available the example datasets used in the book.

Calculate Plant Stress Response Index (PSRI) from time-series
    germination data with optional radicle vigor integration. Built on the
    methodological foundation of the Osmotic Stress Response Index (OSRI)
    framework developed by Walne et al. (2020) <doi:10.1002/agg2.20087>.
    Provides clean, direct PSRI calculations suitable for agricultural research
    and statistical analysis. Note: This package implements methodology
    currently under peer review. Please contact the author before publication
    using this approach.

High-quality real-world data can be transformed into scientific
    real-world evidence for regulatory and healthcare decision-making
    using proven analytical methods and techniques. For example, propensity
    score (PS) methodology can be applied to select a subset of real-world
    data containing patients that are similar to those in the current clinical
    study in terms of baseline covariates, and to stratify the selected
    patients together with those in the current study into more homogeneous
    strata. Then, statistical methods such as the power prior approach or
    composite likelihood approach can be applied in each stratum to draw
    inference for the parameters of interest. This package provides
    functions that implement the PS-integrated real-world 
    evidence analysis methods such as
    Wang et al. (2019) <doi:10.1080/10543406.2019.1657133>,
    Wang et al. (2020) <doi:10.1080/10543406.2019.1684309>, and
    Chen et al. (2020) <doi:10.1080/10543406.2020.1730877>.