I’m pleased to announce that precisely 0.1.0 is now on CRAN! precisely is a study planning tool to calculate sample size based on precision rather than power. Power calculations focus on whether or not an estimate will be statistically significant; calculations of precision are based on the same principles as power calculation but turn the focus to the width of the confidence interval. precisely currently supports sample size calculations for risk differences, rate differences, risk ratios, rate ratios, and odds ratios.
I’m pleased to announce that ymlthis 0.1.0, my project for my summer internship at RStudio, is now on CRAN! ymlthis is a toolkit to reduce the pain of working with YAML in the context of R Markdown documents. The two most common issues when working with YAML are getting the syntax right, particularly the white space, and understanding what options you can specify. ymlthis solves these problems by building and documenting YAML from R.
I’m pleased to announce that ggdag 0.2.0 is now on CRAN! ggdag links the dagitty package, which contains powerful algorithms for analyzing causal DAGs, with the unlimited flexibility of ggplot2. ggdag coverts dagitty objects to a tidy DAG data structure, which allows you to both analyze your DAG and plot it easily in ggplot2. Let’s look at an example for a causal diagram of the effect of smoking on cardiac arrest.
I’m pleased to announce the CRAN release of partition 0.1.0. partition is a fast and flexible data reduction framework that minimizes information loss and creates interpretable clusters. partition uses agglomorative clustering: it starts from the ground up, matching pairs of variables and assessing the amount of information that would be explained by their reduction. If the information is above this user-specified threshold, the data is reduced. This type of reduction is particularly useful in very redundant data, such as high-resolution genetic data.
I’m pleased to announce the release of ggdag 0.1.0 on CRAN! ggdag uses the powerful dagitty package to create and analyze structural causal models and plot them using ggplot2 and ggraph in a tidy, consistent, and easy manner. You can use dagitty objects directly in ggdag, but ggdag also includes wrappers to make DAGs using a more R-like syntax:
# install.packages("ggdag") library(ggdag) dag <- dagify(y ~ x + z, x ~ z) %>% tidy_dagitty() dag ## # A DAG with 3 nodes and 3 edges ## # ## # A tibble: 4 x 8 ## name x y direction to xend yend circular ## <chr> <dbl> <dbl> <fct> <chr> <dbl> <dbl> <lgl> ## 1 x 6.