## Introduction

LongDat R package takes longitudinal dataset as input data and
analyzes if there is significant change of the features over time (proxy
for treatments), while detects and controls for covariates at the same
time. LongDat is able to take in several data types as input, including
count, proportion, binary, ordinal and continuous data. The output table
contains p values, effect sizes and covariates of each feature, making
the downstream analysis easy.

## Install

Install LongDat by typing `install.packages("LongDat")`

in
R.

If you encounter errors like the one below when installing the
package

`Error: 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 to
install LongDat again. An example to this kind of problem and solution
can be found here

## Tutorial

Tutorials for the analysis on continuous time variable (e.g. days)
can be found here.

Tutorials for the analysis on discrete time variable
(e.g. before/after treatment) can be found here.

Alternatively, you can type `browseVignettes(“LongDat”)`

in R after installing LongDat to access these tutorials.

## Citation

The paper will be added here once it is published. Before that,
please cite:

Chen et al., ( 2022 ). LongDat: an R package for confound-sensitive
longitudinal analysis on multi-omics data.