Quantifying Systematic Heterogeneity in Meta-Analysis (getmstatistic)
Quantifying systematic heterogeneity in meta-analysis using R. The \code{M} statistic aggregates heterogeneity information across multiple variants to, identify systematic heterogeneity patterns and their direction of effect in meta-analysis. It’s primary use is to identify outlier studies, which either show ‘null’ effects or consistently show stronger or weaker genetic effects than average across, the panel of variants examined in a GWAS meta-analysis. In contrast to conventional heterogeneity metrics (Q-statistic, I-squared and tau-squared) which measure random heterogeneity at individual variants, \code{M} measures systematic (non-random) heterogeneity across multiple independently associated variants. Systematic heterogeneity can arise in a meta-analysis due to differences in the study characteristics of participating studies. Some of the differences may include: ancestry, allele frequencies, phenotype definition, age-of-disease onset, family-history, gender, linkage disequilibrium and quality control thresholds. See <https://…/> for statistical statistical theory, documentation and examples.

3D Interactive Globes (webglobe)
Displays geospatial data on an interactive 3D globe in the web browser.

Generalized Linear Mixed Model Trees (glmertree)
Recursive partitioning based on (generalized) linear mixed models (GLMMs) combining lmer()/glmer() from lme4 and lmtree()/glmtree() from partykit.

Local Control: An R Package for Generating High Quality Comparative Effectiveness Evidence (LocalControl)
Implements novel nonparametric approaches to address biases and confounding when comparing treatments or exposures in observational studies of outcomes. While designed and appropriate for use in studies involving medicine and the life sciences, the package can be used in other situations involving outcomes with multiple confounders. The package implements a family of methods for nonparametric bias correction when comparing treatments in cross-sectional, case-control, and survival analysis settings, including competing risks with censoring. The approach extends to bias-corrected personalized predictions of treatment outcome differences, and analysis of heterogeneity of treatment effect-sizes across patient subgroups.

Analysis of Factorial Experiments (afex)
Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed between-within (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex uses type 3 sums of squares as default (imitating commercial statistical software).

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