Showing 6 of total 6 results (show query)
insitro
AllelicSeries:Allelic Series Test
Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, O’Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) "An allelic series rare variant association test for candidate gene discovery" <doi:10.1016/j.ajhg.2023.07.001>.
Maintained by Zachary McCaw. Last updated 2 months ago.
13 stars 6.97 score 8 scripts
pingyangchen
milr:Multiple-Instance Logistic Regression with LASSO Penalty
The multiple instance data set consists of many independent subjects (called bags) and each subject is composed of several components (called instances). The outcomes of such data set are binary or categorical responses, and, we can only observe the subject-level outcomes. For example, in manufacturing processes, a subject is labeled as "defective" if at least one of its own components is defective, and otherwise, is labeled as "non-defective". The 'milr' package focuses on the predictive model for the multiple instance data set with binary outcomes and performs the maximum likelihood estimation with the Expectation-Maximization algorithm under the framework of logistic regression. Moreover, the LASSO penalty is attached to the likelihood function for simultaneous parameter estimation and variable selection.
Maintained by Ping-Yang Chen. Last updated 4 years ago.
lasso-penaltymachine-learningopenblascpp
10 stars 4.88 score 15 scripts
hhhelfer
HCmodelSets:Regression with a Large Number of Potential Explanatory Variables
Software for performing the reduction, exploratory and model selection phases of the procedure proposed by Cox, D.R. and Battey, H.S. (2017) <doi:10.1073/pnas.1703764114> for sparse regression when the number of potential explanatory variables far exceeds the sample size. The software supports linear regression, likelihood-based fitting of generalized linear regression models and the proportional hazards model fitted by partial likelihood.
Maintained by H. Battey. Last updated 2 years ago.
2 stars 4.00 score 5 scripts
pedroharaujo
MultipleBubbles:Test and Detection of Explosive Behaviors for Time Series
Provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) <doi:10.1111/iere.12131>. Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.
Maintained by Pedro Araujo. Last updated 7 years ago.
5 stars 1.70 score 10 scripts
cran
HDGLM:Tests for High Dimensional Generalized Linear Models
Test the significance of coefficients in high dimensional generalized linear models.
Maintained by Bin Guo. Last updated 9 years ago.
1.00 score