Description: An implementation of "Bundle Methods for Regularized Risk Minimization" (BMRM) inspired by work of Teo et al., JMLR 2010. This universal data mining framework is particularly useful for structured prediction, classification and regression on big data. The package implements L1 and L2 regularization for: linear SVM, multiclass SVM, f-beta optimization, ordinal regression, quantile regression, epsilon insensitive regression, least mean square, logistic regression, least absolute deviation (see package examples). Other extensions are possible by implementing custom loss functions.

Description: An implementation of "Bundle Methods for Regularized Risk Minimization" (BMRM) inspired by work of Teo et al., JMLR 2010. This universal data mining framework is particularly useful for structured prediction, classification and regression on big data. The package implements L1 and L2 regularization for: linear SVM, multiclass SVM, f-beta optimization, ROC optimization, ordinal regression, quantile regression, epsilon insensitive regression, least mean square, logistic regression, least absolute deviation (see package examples). Other extensions are possible by implementing custom loss functions.

Imports: clpAPI (>= 1.2.2), kernlab (>= 0.9-19), methods

Description: Bundle methods for minimization of convex and non-convex risk
under L1 or L2 regularization. Implements the algorithm proposed by Teo et
al. (JMLR 2010) as well as the extension proposed by Do and Artieres (JMLR
2012). The package comes with lot of loss functions for machine learning
which make it powerful for big data analysis. The applications includes:
structured prediction, linear SVM, multi-class SVM, f-beta optimization,
ROC optimization, ordinal regression, quantile regression,
epsilon insensitive regression, least mean square, logistic regression,
least absolute deviation regression (see package examples), etc... all with

Imports: methods, lpSolve, LowRankQP, matrixStats, Rcpp

Title: Bayesian Multilevel Single Case Models using 'Stan'
Authors@R: person("Michele", "Scandola", email = "michele.scandola@univr.it",
Description: Analyse single case analyses against a control group.
Its purpose is to provide a flexible, with good power and
approach that can manage at the same time controls' and patient's data.
The use of Bayesian statistics allows to test both the alternative and
Scandola, M., & Romano, D. (2020, August 3). <doi:10.31234/osf.io/sajdq>.
Depends: R (>= 3.5.0), rstan, logspline, bayesplot, LaplacesDemon,
Suggests: reshape2, gridExtra, loo, mcmcse, bridgesampling, testthat,