Displaying 1 to 9 from 9 results

Please cite our JMLR paper [bibtex]. Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately. An overview of all mlr related publications can be found here.

machine-learning data-science tuning cran r-package predictive-modeling classification regression statistics r survival-analysis imbalance-correction tutorial mlr learners hyperparameters-optimization feature-selection multilabel-classification clustering stackinglifelines is a pure Python implementation of the best parts of survival analysis. We'd love to hear if you are using lifelines, please leave an Issue and let us know your thoughts on the library. from the command line.

survival-analysis statistics data-sciencehdnom creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data. Browse the vignettes to start.

high-dimensional-data survival-analysis benchmark penalized-cox-models linear-regression nomogram-visualizationscikit-survival is a Python module for survival analysis built on top of scikit-learn. It allows doing survival analysis while utilizing the power of scikit-learn, e.g., for pre-processing or doing cross-validation. The objective in survival analysis (also referred to as reliability analysis in engineering) is to establish a connection between covariates and the time of an event. What makes survival analysis differ from traditional machine learning is the fact that parts of the training data can only be partially observed – they are censored.

survival-analysis machine-learning scikit-learnThis is a place for miscellaneous R and other code I've put together for clients, co-workers or myself for learning and demonstration purposes. The attempt is made to put together some well-commented and/or conceptually clear code from scratch, though most functionality is readily available in any number of well-developed R packages. Typically, examples are provided using such packages for comparison of results. I would say most of these are geared toward intermediate to advanced folks that want to dig a little deeper into the models and underlying algorithms. More recently, if it gets more involved, I usually just create a document of some kind rather than a standard *.R file, so you might check out the docs repo as well.

r stan jags matlab julia bayesian mixed-models gaussian-processes factor-analysis pca em survival-analysis ordinal-regression probit irt mixture-model zip lasso-regression additive-modelsPySurvival is an open source python package for Survival Analysis modeling - the modeling concept used to analyze or predict when an event is likely to happen. It is built upon the most commonly used machine learning packages such NumPy, SciPy and PyTorch. PySurvival is compatible with Python 2.7-3.7.

survival-analysis machine-learning deep-learning pytorch numpyGenerates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs. Supports generation and evaluation of mixture and split/split-split/N-split plot designs. Includes parametric and Monte Carlo power evaluation functions. Provides a framework to evaluate power using functions provided in other packages or written by the user.

optimal-designs rstats design-of-experiments survival-analysis power r split-plot-designs linear-models linear-regression monte-carloA Julia package for performing survival analysis.

julia statistics survival-analysis time-to-eventsurvtmle is an R package designed to use targeted minimum loss-based estimation (TMLE) to compute covariate-adjusted marginal cumulative incidence estimates in right-censored survival settings with and without competing risks. The estimates can leverage ensemble machine learning via the SuperLearner package. If you encounter any bugs or have any specific feature requests, please file an issue.

survival-analysis tmle competing-risks ensemble-learning
We have large collection of open source products. Follow the tags from
Tag Cloud >>

Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
**Add Projects.**