Transfer learning for generalized factor models with support for continuous, count (Poisson), and binary data types. The package provides functions for single and multiple source transfer learning, source detection to identify positive and negative transfer sources, factor decomposition using Maximum Likelihood Estimation (MLE), and information criteria ('IC1' and 'IC2') for rank selection. The methods are particularly useful for high-dimensional data analysis where auxiliary information from related source datasets can improve estimation efficiency in the target domain.

Analyse data from longitudinal studies to characterise changes in values of
   semi-quantitative outcome variables within individual subjects, using high performance C++
   code to enable rapid processing of large datasets. A flexible methodology is available for
   codifying these state transitions.

Translation of logit models coefficients into percentages, following Deauvieau (2010) <doi:10.1177/0759106309352586>.

Provides a robust and user-friendly solution for transliterating Ukrainian strings into Latin symbols.

An object model for source text and translations. Find and extract
    translatable strings. Provide translations and seamlessly retrieve them at
    runtime.

To make the semiparametric transformation models easier to apply in real studies, 
    we introduce this R package, in which the MLE in transformation models via 
    an EM algorithm proposed by Zeng D, Lin DY(2007) <doi:10.1111/j.1369-7412.2007.00606.x> 
    and adaptive lasso method in transformation models proposed by Liu XX, Zeng 
    D(2013) <doi:10.1093/biomet/ast029> are implemented.  
    C++ functions are used to compute complex loops. The coefficient vector and 
    cumulative baseline hazard function can be estimated, along with the 
    corresponding standard errors and P values.

Treatment and visualization of membrane (selective) transport 
    data. Transport profiles involving up to three species are produced as
    publication-ready plots and several membrane performance parameters 
    (e.g. separation factors as defined in Koros et al. (1996) 
    <doi:10.1351/pac199668071479> and non-linear regression parameters
    for the equations described in Rodriguez de San Miguel et al. (2014)
    <doi:10.1016/j.jhazmat.2014.03.052>) can be obtained. Many widely used 
    experimental setups (e.g. membrane physical aging) can be easily studied
    through the package's graphical representations.