All Projects → PharmCat → Metida.jl

PharmCat / Metida.jl

Licence: MIT license
Julia package for fitting mixed-effects models with flexible random/repeated covariance structure.

Programming Languages

julia
2034 projects

Projects that are alternatives of or similar to Metida.jl

ARCHModels.jl
A Julia package for estimating ARMA-GARCH models.
Stars: ✭ 63 (+231.58%)
Mutual labels:  regression, statistical-models
ara
Agile Regression Analyzer
Stars: ✭ 74 (+289.47%)
Mutual labels:  regression
LIBSVM.jl
LIBSVM bindings for Julia
Stars: ✭ 74 (+289.47%)
Mutual labels:  regression
The-Supervised-Learning-Workshop
An Interactive Approach to Understanding Supervised Learning Algorithms
Stars: ✭ 24 (+26.32%)
Mutual labels:  regression
jGeneticNeuralNet
A Java library that trains neural networks with a genetic algorithm.
Stars: ✭ 16 (-15.79%)
Mutual labels:  regression
needlestack
Multi-sample somatic variant caller
Stars: ✭ 45 (+136.84%)
Mutual labels:  regression
cheapml
Machine Learning algorithms coded from scratch
Stars: ✭ 17 (-10.53%)
Mutual labels:  regression
generalized-additive-models-workshop-2019
A workshop on using generalized additive models and the mgcv package.
Stars: ✭ 23 (+21.05%)
Mutual labels:  mixed-models
smooth
The set of smoothing functions used for time series analysis and in forecasting.
Stars: ✭ 78 (+310.53%)
Mutual labels:  regression
basis-expansions
Basis expansion transformers in sklearn style.
Stars: ✭ 74 (+289.47%)
Mutual labels:  regression
blorr
Tools for developing binary logistic regression models
Stars: ✭ 16 (-15.79%)
Mutual labels:  regression
Cuff less BP Prediction
Prediction of Blood Pressure from ECG and PPG signals using regression methods.
Stars: ✭ 101 (+431.58%)
Mutual labels:  regression
PyTsetlinMachineCUDA
Massively Parallel and Asynchronous Architecture for Logic-based AI
Stars: ✭ 37 (+94.74%)
Mutual labels:  regression
psyplot
Python package for interactive data visualization
Stars: ✭ 64 (+236.84%)
Mutual labels:  regression
EvoTrees.jl
Boosted trees in Julia
Stars: ✭ 108 (+468.42%)
Mutual labels:  regression
ML-Track
This repository is a recommended track, designed to get started with Machine Learning.
Stars: ✭ 19 (+0%)
Mutual labels:  regression
SGpp
SG⁺⁺ – the numerical library for Sparse Grids in all their variants.
Stars: ✭ 59 (+210.53%)
Mutual labels:  regression
SMLP2021
Notebooks for SMLP2021
Stars: ✭ 23 (+21.05%)
Mutual labels:  mixed-models
GPBoost
Combining tree-boosting with Gaussian process and mixed effects models
Stars: ✭ 360 (+1794.74%)
Mutual labels:  mixed-effects
Cubist
A Python package for fitting Quinlan's Cubist regression model
Stars: ✭ 22 (+15.79%)
Mutual labels:  regression

Metida

This program comes with absolutely no warranty. No liability is accepted for any loss and risk to public health resulting from use of this software.

Version pkgeval Cover Build Docs
version pkgeval codecov Tier 1 Latest docs Stable docs

Metida.jl is Julia package for fitting mixed-effects models with flexible covariance structure.

Install:

import Pkg; Pkg.add("Metida")

Using:

using Metida, CSV, DataFrames, CategoricalArrays
df = CSV.File(joinpath(dirname(pathof(Metida)),"..","test","csv","df0.csv")) |> DataFrame
transform!(df, :subject => categorical, renamecols=false)
transform!(df, :period => categorical, renamecols=false)
transform!(df, :sequence => categorical, renamecols=false)
transform!(df, :formulation => categorical, renamecols=false)

lmm = LMM(@formula(var~sequence+period+formulation), df;
random = VarEffect(@covstr(formulation|subject), CSH),
repeated = VarEffect(@covstr(formulation|subject), DIAG),
)

fit!(lmm)

# Or you can use macro @lmmformula

lmm = LMM(@lmmformula(var~sequence+period+formulation,
    random = formulation|subject:CSH,
    repeated = formulation|subject:DIAG),
    df0)
fit!(lmm)

Also you can use this package with MatidaNLopt.jl and MetidaCu.jl.

See also MixedModels.jl: powerful package for mixed models.

Copyright © 2020 Metida Author: Vladimir Arnautov [email protected]

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].