R-universe search: mxl

Showing 4 of total 4 results (show query)

jhelvy

logitr:Logit Models w/Preference & WTP Space Utility Parameterizations

Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) <doi:10.1017/CBO9780511805271>. More details can be found in Helveston (2023) <doi:10.18637/jss.v105.i10>.

Maintained by John Helveston. Last updated 5 months ago.

log-likelihood logit logit-model mixed-logit mlogit multinomial-regression mxl mxl-models preference-space preferences willingness-to-pay wtp

19.3 match 54 stars 9.10 score 119 scripts 1 dependents

benrenard

musicXML:Data Sonification using 'musicXML'

A set of tools to facilitate data sonification and handle the 'musicXML' format <https://usermanuals.musicxml.com/MusicXML/Content/XS-MusicXML.htm>. Several classes are defined for basic musical objects such as note pitch, note duration, note, measure and score. Moreover, sonification utilities functions are provided, e.g. to map data into musical attributes such as pitch, loudness or duration. A typical sonification workflow hence looks like: get data; map them to musical attributes; create and write the 'musicXML' score, which can then be further processed using specialized music software (e.g. 'MuseScore', 'GuitarPro', etc.). Examples can be found in the blog <https://globxblog.github.io/>, the presentation by Renard and Le Bescond (2022, <https://hal.science/hal-03710340v1>) or the poster by Renard et al. (2023, <https://hal.inrae.fr/hal-04388845v1>).

Maintained by Benjamin Renard. Last updated 26 days ago.

musicxml sonification

13.6 match 4.30 score 8 scripts

bioc

CNVrd2:CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data.

CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions.

Maintained by Hoang Tan Nguyen. Last updated 5 months ago.

copynumbervariation snp sequencing software coverage linkagedisequilibrium clustering.jags cpp

7.0 match 3 stars 4.92 score

bioc

GENESIS:GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness

The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes.

Maintained by Stephanie M. Gogarten. Last updated 1 months ago.

snp geneticvariability genetics statisticalmethod dimensionreduction principalcomponent genomewideassociation qualitycontrol biocviews

3.2 match 36 stars 10.44 score 342 scripts 1 dependents