LongDat R package takes longitudinal dataset as input data andanalyzes if there is significant change of the features over time (proxyfor treatments), while detects and controls for covariates at the sametime. LongDat is able to take in several data types as input, includingcount, proportion, binary, ordinal and continuous data. The output tablecontains p values, effect sizes and covariates of each feature, makingthe downstream analysis easy.
Install LongDat by typinginstall.packages("LongDat") inR.
If you encounter errors like the one below when installing thepackageError: package or namespace load failed for ‘LongDat’ object ‘A’ is not exported by 'namespace:B_package'
please try install the dependency B_package first, and then try toinstall LongDat again. An example to this kind of problem and solutioncan be foundhere
Tutorials for the analysis on continuous time variable (e.g. days)can be foundhere.
Tutorials for the analysis on discrete time variable(e.g. before/after treatment) can be foundhere.
Alternatively, you can typebrowseVignettes(“LongDat”)in R after installing LongDat to access these tutorials.
Chia-Yu Chen, Ulrike Löber, Sofia K Forslund, LongDat: an R packagefor covariate-sensitive longitudinal analysis of high-dimensional data,Bioinformatics Advances, Volume 3, Issue 1, 2023, vbad063,https://doi.org/10.1093/bioadv/vbad063