Make graphical representations of single case data and transform graphical displays back to raw data, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>. The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend.

Provides an efficient framework for 
   high-dimensional linear and diagonal discriminant analysis with 
   variable selection.  The classifier is trained using James-Stein-type 
   shrinkage estimators and predictor variables are ranked using 
   correlation-adjusted t-scores (CAT scores).  Variable selection error 
   is controlled using false non-discovery rates or higher criticism.

Functions and Datasets from Lohr, S. (1999), Sampling:
        Design and Analysis, Duxbury.

We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2020), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", <arXiv:2002.11992>.

Provides digital tools for performing analyses within Social Dynamics and complexity in the Ancient Mediterranean (SDAM), which is a research group based at the Department of History and Classical Studies at Aarhus University.

Data sets and functions to support the books "Statistics: Data 
        analysis and modelling" by Speekenbrink, M. (2021) 
        <https://mspeekenbrink.github.io/sdam-book/> and "An R companion to 
        Statistics: data analysis and modelling" by Speekenbrink, M. (2021) 
        <https://mspeekenbrink.github.io/sdam-r-companion/>. All datasets 
        analysed in these books are provided in this package. In addition, the 
        package provides functions to compute sample statistics (variance, 
        standard deviation, mode), create raincloud and enhanced Q-Q plots, and 
        expand Anova results into omnibus tests and tests of individual 
        contrasts.