Joint Analysis and Imputation of generalized linear models and linear mixed models with missing values
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Updated
Apr 2, 2024 - R
Joint Analysis and Imputation of generalized linear models and linear mixed models with missing values
🎓 Tidy regression tools for academics
Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
Fitting dose-response models in R
Modelling DNA methylation profiles
Sorted L1 Penalized Estimation
gnm package for generalized nonlinear models in R
R package: Linear Splines with Convenient Parameterizations
A Bioconductor package and shiny app for DNA methylation data length bias adjustment in gene set testing
Predicting chemosensitivity using gene expression
Constrained likelihood estimation and inference with truncated lasso penalty for linear, generalized linear, and Gaussian graphical models.
Generalized Linear Models with the Exclusive Lasso Penalty
Fast Sparse Linear Models for Big Data with SAGA
Ribo-DT is a snakemake pipeline to infer single-codon and codon-pair dwell times as well as gene flux from ribosome profiling data using generalized linear model (GLM) with negative binomial noise model (Gobet et al., PNAS, 2020).
Build and Tune Several Models
Analysis of data from the Framingham Heart Study using generalized linear models.
Negative Binomial Additive Model for RNASeq Data
Various techniques applied for the prediction of bankruptcy- Generalized Linear Regression- Logistic, Classification Tree, Generalized Additive Model, Linear Discriminant Analysis and Neural Networks. Evaluation criteria used was misclassifcation errors.
Homework 2 for the INTL 601 Quantitative Research Methods Course, Prof. David Carlson, Koç University.
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