Subtracting Summary Statistics of One or more Cohorts from Meta-GWAS Results (MetaSubtract)
If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. The validation cohort needs to be independent from the meta-GWAS results. MetaSubtract will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. It can be used for whole GWAS, but also for a limited set of SNPs or other genetic markers.

Dynamic Model Averaging and Dynamic Model Selection for Continuous Outcomes (fDMA)
It allows to estimate Dynamic Model Averaging, Dynamic Model Selection and Median Probability Model. The original methods (see References) are implemented, as well as, selected further modifications of these methods. In particular the User might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using Google Trends. The code is written in a way which minimises the computational burden (which is quite an obstacle for Dynamic Model Averaging if many variables are used). For example, this package allows for parallel computations under Windows machines. Additionally, the User might reduce a set of models according to a certain algorithm. The package is designed in a way that is hoped to be especially useful in economics and finance. (Research funded by the Polish National Science Centre grant under the contract number DEC-2015/19/N/HS4/00205.)

Spatiotemporal Boundary Detection Model for Areal Unit Data (womblR)
Implements a spatiotemporal boundary detection model with a dissimilarity metric for areal data with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and spatial correlation is introduced at each time point through a conditional autoregressive (CAR) prior. Temporal correlation is introduced through a hierarchical structure and can be specified as exponential or first-order autoregressive. Full details of the package can be found in the accompanying vignette.

A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem (mcMST)
Algorithms to approximate the Pareto-front of multi-criteria minimum spanning tree problems. Additionally, a modular toolbox for the generation of multi-objective benchmark graph problems is included.

Stepwise Variable Selection Procedures for Regression Analysis (My.stepwise)
The stepwise variable selection procedure (with iterations between the ‘forward’ and ‘backward’ steps) can be used to obtain the best candidate final regression model in regression analysis. All the relevant covariates are put on the ‘variable list’ to be selected. The significance levels for entry (SLE) and for stay (SLS) are usually set to 0.15 (or larger) for being conservative. Then, with the aid of substantive knowledge, the best candidate final regression model is identified manually by dropping the covariates with p value > 0.05 one at a time until all regression coefficients are significantly different from 0 at the chosen alpha level of 0.05.