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easystats
parameters:Processing of Model Parameters
Utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution).
Maintained by Daniel Lüdecke. Last updated 10 days ago.
betabootstrapciconfidence-intervalsdata-reductioneasystatsfafeature-extractionfeature-reductionhacktoberfestparameterspcapvaluesregression-modelsrobust-statisticsstandardizestandardized-estimatesstatistical-models
454 stars 15.67 score 1.8k scripts 56 dependents
victor-navarro
calmr:Canonical Associative Learning Models and their Representations
Implementations of canonical associative learning models, with tools to run experiment simulations, estimate model parameters, and compare model representations. Experiments and results are represented using S4 classes and methods.
Maintained by Victor Navarro. Last updated 10 months ago.
3 stars 6.40 score 17 scripts
smwindecker
mixchar:Mixture Model for the Deconvolution of Thermal Decay Curves
Deconvolution of thermal decay curves allows you to quantify proportions of biomass components in plant litter. Thermal decay curves derived from thermogravimetric analysis (TGA) are imported, modified, and then modelled in a three- or four- part mixture model using the Fraser-Suzuki function. The output is estimates for weights of pseudo-components corresponding to hemicellulose, cellulose, and lignin. For more information see: Müller-Hagedorn, M. and Bockhorn, H. (2007) <doi:10.1016/j.jaap.2006.12.008>, Órfão, J. J. M. and Figueiredo, J. L. (2001) <doi:10.1016/S0040-6031(01)00634-7>, and Yang, H. and Yan, R. and Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006) <doi:10.1021/ef0580117>.
Maintained by Saras Windecker. Last updated 2 years ago.
10 stars 5.26 score 18 scripts
bioc
betaHMM:A Hidden Markov Model Approach for Identifying Differentially Methylated Sites and Regions for Beta-Valued DNA Methylation Data
A novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites.
Maintained by Koyel Majumdar. Last updated 3 months ago.
dnamethylationdifferentialmethylationimmunooncologybiomedicalinformaticsmethylationarraysoftwaremultiplecomparisonsequencingspatialcoveragegenetargethiddenmarkovmodelmicroarray
4.18 score
dyfanjones
sagemaker.mlcore:sagemaker machine learning core classes and methods
`sagemaker` machine learning core classes and methods.
Maintained by Dyfan Jones. Last updated 3 years ago.
amazon-sagemakerawsmachine-learningsagemakersdk
2.65 score 3 dependents