Read Exported Data from ‘SoftMax Pro’ (softermax)
Read microtiter plate data and templates exported from Molecular Devices ‘SoftMax Pro’ software <https://…/softmax-pro-7-software>. Data exported by ‘SoftMax Pro’ version 5.4 and greater are supported.

Fit Repeated Linear Regressions (fRLR)
When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. This package aims to fit a set of repeated linear regressions faster. More details can be found in this blog Lijun Wang (2017) <https://…/>.

Simulate Longitudinal Dataset with Time-Varying Correlated Covariates (SimTimeVar)
Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. <arXiv:1709.10074> for methodological details.

Draws Overview of Outliers (O3) Plot (OutliersO3)
Potential outliers are identified for all combinations of a dataset’s variables. The available methods are HDoutliers() from the package ‘HDoutliers’, FastPCS() from the package ‘FastPCS’, mvBACON() from ‘robustX’, adjOutlyingness() from ‘robustbase’, DectectDeviatingCells() from ‘cellWise’.

Generalized Pair Hidden Markov Chain Model for Sequence Alignment (gphmm)
Implementation of a generalized pair hidden Markov chain model (GPHMM) that can be used to compute the probability of alignment between two sequences of nucleotides (e.g., a reference sequence and a noisy sequenced read). The model can be trained on a dataset where the noisy sequenced reads are known to have been sequenced from known reference sequences. If no training sets are available default parameters can be used.

Multivariate Symmetric Uncertainty and Other Measurements (msu)
Estimators for multivariate symmetrical uncertainty based on the work of Gustavo Sosa et al. (2016) <arXiv:1709.08730>, total correlation, information gain and symmetrical uncertainty of categorical variables.