**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.

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