RStudio Expertise and Consulting
RStudio is a popular open-source software platform for data science, including statistical consulting. It provides a comprehensive set of tools and resources for data manipulation, visualization, and statistical analysis, and is widely used by statisticians and data scientists for consulting projects.
RStudio offers a range of features and functionality that make it well-suited for statistical consulting, including:
- Integrated development environment (IDE) for writing and running code
- Support for a wide range of statistical languages and packages, including R, Python, and Shiny
- Visualization tools for creating interactive graphs and charts
- Collaboration and sharing capabilities, including support for version control and project management
Overall, RStudio is a powerful and popular platform for statistical consulting, offering a comprehensive set of tools and resources for data manipulation, visualization, and statistical analysis.
Learn about the packages
NLME (nonlinear mixed effects)
The nlme (nonlinear mixed effects) package is a popular R package for fitting and analyzing mixed-effects models. It is commonly used in biostatistics and other fields where data with both fixed and random effects need to be analyzed.
The nlme package allows for the estimation of both fixed and random effects, and provides a range of tools and functionality for model fitting, evaluation, and prediction. It also offers support for a variety of model structures, including linear, nonlinear, and generalized linear mixed models.
In summary, the nlme package is a powerful tool for fitting and analyzing mixed-effects models in R. It provides a range of features and functionality for estimating fixed and random effects, and is widely used in biostatistics and other fields where data with complex structures need to be analyzed.
LME4 package
The lme4 package is a popular R package for fitting and analyzing linear mixed-effects models. It is commonly used in biostatistics and other fields where data with both fixed and random effects need to be analyzed.
The lme4 package allows for the estimation of both fixed and random effects, and provides a range of tools and functionality for model fitting, evaluation, and prediction. It specifically focuses on linear mixed models, which assume a linear relationship between the response and predictor variables.
In summary, the lme4 package is a powerful tool for fitting and analyzing linear mixed-effects models in R. It provides a range of features and functionality for estimating fixed and random effects, and is widely used in biostatistics and other fields where data with complex structures need to be analyzed.
emmeans package
The emmeans (estimated marginal means) package is a popular R package for computing and comparing marginal means in a variety of model types. It is commonly used in biostatistics and other fields where researchers need to make inferences about a population based on sample data.
The emmeans package provides a range of tools and functionality for computing and comparing marginal means, including support for a variety of model types such as linear, generalized linear, and mixed-effects models. It also offers a range of options for adjusting for multiple comparisons, as well as visualizing and comparing the estimated marginal means.
In summary, the emmeans package is a powerful tool for computing and comparing marginal means in R. It provides a range of features and functionality for making inferences about a population based on sample data, and is widely used in biostatistics and other fields where marginal means need to be estimated and compared.
dplyr package
The dplyr package is an R package that provides a set of verbs for working with data frames, such as filtering, selecting, and summarizing data. It is part of the tidyverse collection of R packages, which provides tools for data manipulation, visualization, and modeling.
tidyr packages
The tidyr packages are a collection of R packages that provide tools for transforming and tidying data frames, such as reshaping, pivoting, and joining data. It is part of the tidyverse collection of R packages, which provides tools for data manipulation, visualization, and modeling.