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Mar 16, 2022 - Python
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survival-analysis
Here are 300 public repositories matching this topic...
Survival analysis in Python
python
data-science
statistics
survival-analysis
cox-regression
maximum-likelihood
reliability-analysis
Machine Learning in R
data-science
machine-learning
cran
tutorial
r
statistics
clustering
regression
feature-selection
tuning
classification
survival-analysis
r-package
hyperparameters-optimization
predictive-modeling
imbalance-correction
mlr
learners
stacking
multilabel-classification
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Mar 20, 2022 - R
Survival analysis built on top of scikit-learn
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Mar 8, 2022 - Python
Open source package for Survival Analysis modeling
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Feb 14, 2022 - HTML
Improving XGBoost survival analysis with embeddings and debiased estimators
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Mar 3, 2022 - Python
This repository contains morden baysian statistics and deep learning based research articles , software for survival analysis
machine-learning
deep-learning
time-series
healthcare
survival-analysis
bayesian-inference
gaussian-processes
cancer-research
time-to-event
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Feb 18, 2021
Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. Now almost entirely superseded by the models-by-example repo.
python
r
julia
zip
matlab
irt
pca
survival-analysis
bayesian
stan
em
mixture-model
factor-analysis
gaussian-processes
jags
mixed-models
additive-models
lasso-regression
ordinal-regression
probit
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Nov 25, 2020 - R
Reliability engineering toolkit for Python - https://reliability.readthedocs.io/en/latest/
python
data-science
statistics
simulation
reliability-engineering
modeling
survival-analysis
maximum-likelihood-estimation
weibull-analysis
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Feb 1, 2022 - Python
GPstuff - Gaussian process models for Bayesian analysis
regression
octave
classification
survival-analysis
bayesian
spatial-analysis
bayesian-inference
expectation-propagation
mcmc
gaussian-processes
variational-inference
bayesian-optimization
covariance-functions
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Sep 24, 2021 - MATLAB
Deep Recurrent Survival Analysis, an auto-regressive deep model for time-to-event data analysis with censorship handling. An implementation of our AAAI 2019 paper and a benchmark for several (Python) implemented survival analysis methods.
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Jan 28, 2021 - Python
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chiragnagpal
commented
Oct 29, 2020
Currently we do not have notebooks that compare performance of DSM with other models.
We would need to compare against:
-DeepHit
-DeepSurv
-Random Survival Forest
-Cox PH
on Time Dependent CI and Brier Score.
Generates 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.
r
monte-carlo
linear-regression
power
rstats
survival-analysis
linear-models
design-of-experiments
optimal-designs
split-plot-designs
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Mar 10, 2022 - R
COX Proportional risk model and survival analysis implemented by tensorflow.
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Mar 5, 2021 - Python
RaphaelS1
commented
Dec 18, 2020
Following changes are required:
- Remove composition from
cranktodistr- This doesn't make any sense for abstract rankings, composition can only make sense forlptodistr. - Add composition from
responsetodistr- This can be most efficiently done by abstracting the probabilistic regression composition and using the same functions in both
(1) is higher priority as its resul
sql
graphics-engine
hadoop
interpolation
domain-driven-design
oracle
survival-analysis
kaplan-meier
readers
n-layer
annuity
domain-driven-designstyle
z-spread
chapman-kolmogorov
black-and-scholes-pricing
miningstructure-reader
framework-actuarial-reporting
fluid-reporting
householderqr
self-augmenting-mining-structures
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Nov 29, 2021 - C#
Deep learning for flexible market price modeling (landscape forecasting) in real-time bidding advertising. An implementation of our KDD 2019 paper with some other (Python) implemented prediction models.
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Nov 11, 2020 - Python
Survival analysis in Julia
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Feb 9, 2022 - Julia
Joint Models for Longitudinal and Survival Data using MCMC
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Jan 28, 2021 - R
SALMON: Survival Analysis Learning with Multi-Omics Neural Networks
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Sep 2, 2020 - Python
A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.
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Jan 21, 2022 - Python
Scripts for https://www.nature.com/articles/s41598-018-27707-4, using Convolutional Neural Network to detect lung cancer tumor area
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Apr 8, 2021 - Python
Piece-wise exponential Additive Mixed Modeling tools
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Mar 11, 2022 - R
Benchmarking and Visualization Toolkit for Penalized Cox Models
benchmark
linear-regression
high-dimensional-data
survival-analysis
penalized-cox-models
nomogram-visualization
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Dec 21, 2021 - R
Survival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.
uncertainty
survival-analysis
rstan
health-economic-evaluation
inla
hamiltonian-monte-carlo
survival-models
frequentist
plotting-survival-curves
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Jan 15, 2022 - C++
Tutorial on survival analysis using TensorFlow.
tutorial
deep-learning
notebook
tensorflow
survival-analysis
convolutional-neural-networks
time-to-event
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May 16, 2020 - Jupyter Notebook
ICML 2018: "Adversarial Time-to-Event Modeling"
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Jun 7, 2018 - Python
Joint Models for Longitudinal & Survival Data under Maximum Likelihood
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Feb 4, 2022 - R
machine-learning
statistics
survival-analysis
longitudinal-data
targeted-learning
causal-inference
precision-medicine
nonparametric-statistics
stochastic-interventions
censored-data
robust-statistics
modified-treatment-policy
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Feb 15, 2022 - R
Slides and notebooks for my tutorial at PyData London 2018
tutorial
deep-learning
optimization
pydata
linear-programming
survival-analysis
gradient-descent
quadratic-programming
convex-optimization
elastic-net
stochastic-gradient-descent
maximum-likelihood-estimation
quantile-regression
multilayer-perceptron
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Jul 2, 2018 - Jupyter Notebook
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Hi,
I ran into the following assertion error when computing the c-index for the discrete MTLR method.
assert durations.shape[0] == surv.shape[1] == surv_idx.shape[0] == events.shape[0]I suppose the error is due to the fact that the maximum of test durations is 1628***, while the function gets in input a number between 0 and 490. This range (0, 490) is the result of applying the following