" Thank you for PROCESS! It is the next best thing to apple pie. " PROCESS has been such a great addition to my research tool kit." - Canada User-friendly results display for the non-statistician. Ease and flexibility of input, with embedded help prompts. " PROCESS is a very user-friendly and marvelous macro." - Turkey The Excel regression analysis solution provides much more functionality and more intuitive results display than the standard regression analysis provided within Excel via the Analysis Toolpak add-in. "I love PROCESS!! Thank you so much for this amazing tool." - United States descended from ue stats miracle." - United States It is a wonderful development in research" - Nigeria PROCESS has solved most of my problems with mediation and moderation since I started carrying out research and analysing such data. "Research with the PROCESS macro tool is fun." - Kenya Thank you for all your efforts!" -The Netherlands "Thanks for inspiring us all to think in terms of mediating and moderating effects but, more importantly, help unleash the power of computational power in a easy to use way. I was stuck with my PhD data before I stumbled on it." – Kenya "Thanks for developing this very nice model for solving moderation. " It is a much easier alternative to doing this in Mplus" - United States "Testing moderated mediation has never been so easy." - Malaysia It allowed me to do things in my dissertation that would have been computationally very difficult for me otherwise." - United States I look forward to reading and learning more about this topic!" - United States "Thank you for creating and sharing the PROCESS macro! I find your book ( Introduction to Mediation, Moderation, and Conditional Process Analysis) and papers about special PROCESS topics very helpful, clear, and easy to follow - a real pleasure to read and apply. "Thank you for providing this excellent software - it is immensely helpful in my research." - United States And it provides a relatively simple way to analyze relatively complex models using bootstrapping CIs." - United States It makes it easier to commit to one structure for analyzing a hypothesized mediation model. To use these, all you need to do is download them and unzip into the "bayesreg" folder."The PROCESS macro is great. Alternatively, for convenience, the pre-compiled MEX files (MATLAB R2017a) for Windows, Linux and Mac OSX can be downloaded from the following URL: To compile the C++ code, run compile.m from the bayesreg directory within MATLAB compilation requires the MS Visual Studio Professional or the GNU g++ compiler. The package now handles logistic regression without the need for MEX files, but big speed-ups can be obtained when using compiled code, so this is recommended. Fix count regression for Matlab 2020a and 2020b releases. High-Dimensional Bayesian Regularised Regression with the BayesReg Package To install the R package, type "install.packages("bayesreg")" within R. An R version of this toolbox is now available on CRAN. Please see the scripts in the directory "examples\" for examples on how to use the toolbox, or type "help bayesreg" within MATLAB. The toolbox is very efficient and can be used with high-dimensional data. Most features are straightforward to use and the toolbox can work directly with MATLAB tables (including automatically handling categorical variables), or you can use standard MATLAB matrices. in running RegressIt on a Mac and to take useful screen shots with information about your computer, your analysis, and errors which may have occurred. To support analysis of data with outliers, we provide two heavy-tailed error models in our implementation of Bayesian linear regression: Laplace and Student-t distribution errors. This can be used to exploit a priori knowledge regarding predictors and how they may be related to each other (for example, in grouping genetic data into genes and collections of genes such as pathways).Ĭount regression is now supported through implementation of Poisson and geometric regression models. The toolbox allows predictors to be assigned to logical groupings (potentially overlapping, so that predictors can be part of multiple groups). The lasso, horseshoe, horseshoe+ and log-t priors are recommended for data sets where the number of predictors is greater than the sample size, and the log-t prior provides adaptation to unknown levels of sparsity. The toolbox provides highly efficient and numerically stable implementations of ridge, lasso, horseshoe, horseshoe+, log-t and g-prior regression. This is a comprehensive, user-friendly toolbox implementing the state-of-the-art in Bayesian linear regression, logistic and count regression.
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