# Software

__R package__

**spcr**: Computes the sparse principal component regression. The regularization parameters are also optimized by cross-validation.- Kawano, S., Fujisawa, H., Takada, T. and Shiroishi, T. (2015) "Sparse principal component regression with adaptive loading"
*Computational Statistics & Data Analysis*, 89, 192-203 (doi: 10.1016/j.csda.2015.03.016 ). - Kawano, S., Fujisawa, H., Takada, T. and Shiroishi, T. (2018) "Sparse principal component regression for generalized linear models"
*Computational Statistics & Data Analysis*, 124, 180-196

- Kawano, S., Fujisawa, H., Takada, T. and Shiroishi, T. (2015) "Sparse principal component regression with adaptive loading"
**sAIC**: Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.- Ninomiya, Y. and Kawano, S. (2016) "AIC for the Lasso in generalized linear models"
*Electronic Journal of Statistics*, 10, 2537-2560 (doi: 10.1214/16-EJS1179).

- Ninomiya, Y. and Kawano, S. (2016) "AIC for the Lasso in generalized linear models"
**neggfl**: Computes the Bayesian generalized fused lasso regression based on a normal-exponential-gamma (NEG) prior distribution.- Shimamura, K., Ueki, M., Kawano, S. and Konishi, S. (2019) "Bayesian generalized fused lasso modeling via NEG distribution"
*Communications in Statistics - Theory and Methods*, 48, 4132-4153 (doi: 10.1080/03610926.2018.1489056).

- Shimamura, K., Ueki, M., Kawano, S. and Konishi, S. (2019) "Bayesian generalized fused lasso modeling via NEG distribution"
**RVSManOpt**: Computes the sparse reduced-rank factor regression based on manifold optimization. This package can perform estimating the rank of the coefficient matrix, selecting the number of explanatory variables which composes factors included in the regression, and selecting the number of the factors are relevant with the response variables.- Yoshikawa, K. and Kawano, S. (2019) "Sparse reduced-rank regression for simultaneous rank and variable selection via manifold optimization" arXiv:1910.05083 [URL].