Showing 64 of total 64 results (show query)

mclements

rstpm2:Smooth Survival Models, Including Generalized Survival Models

R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>, and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.

Maintained by Mark Clements. Last updated 5 months ago.

fortranopenblascpp

8.1 match 28 stars 11.01 score 137 scripts 50 dependents

bioc

pathwayPCA:Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection

pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) <https://doi.org/10.1093/bioinformatics/btn458>; Chen et al. (2010) <https://doi.org/10.1002/gepi.20532>; and Chen (2011) <https://doi.org/10.2202/1544-6115.1697>.

Maintained by Gabriel Odom. Last updated 5 months ago.

copynumbervariationdnamethylationgeneexpressionsnptranscriptiongenepredictiongenesetenrichmentgenesignalinggenetargetgenomewideassociationgenomicvariationcellbiologyepigeneticsfunctionalgenomicsgeneticslipidomicsmetabolomicsproteomicssystemsbiologytranscriptomicsclassificationdimensionreductionfeatureextractionprincipalcomponentregressionsurvivalmultiplecomparisonpathways

3.8 match 11 stars 7.74 score 42 scripts

jmbarbone

fuj:Functions and Utilities for Jordan

Provides core functions and utilities for packages and other code developed by Jordan Mark Barbone.

Maintained by Jordan Mark Barbone. Last updated 6 days ago.

6.0 match 2 stars 4.48 score 8 scripts 1 dependents

bioc

GlobalAncova:Global test for groups of variables via model comparisons

The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany.

Maintained by Manuela Hummel. Last updated 5 months ago.

microarrayonechanneldifferentialexpressionpathwaysregression

3.8 match 5.32 score 9 scripts 1 dependents

jmbarbone

mark:Miscellaneous, Analytic R Kernels

Miscellaneous functions and wrappers for development in other packages created, maintained by Jordan Mark Barbone.

Maintained by Jordan Mark Barbone. Last updated 1 months ago.

3.0 match 6 stars 4.95 score 9 scripts

bioc

Icens:NPMLE for Censored and Truncated Data

Many functions for computing the NPMLE for censored and truncated data.

Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.

infrastructure

3.4 match 3.83 score 16 scripts 7 dependents

cran

EcoVirtual:Simulation of Ecological Models

Computer simulations of classical ecological models as a learning resource.

Maintained by Alexandre Adalardo de Oliveira. Last updated 6 years ago.

5.3 match 1.48 score 1 dependents

repboxr

repboxStata:Repbox analysis of stata scripts in reproduction packages

Repbox analysis of stata scripts in reproduction packages

Maintained by Sebastian Kranz. Last updated 29 days ago.

1.9 match 2.82 score 4 scripts 2 dependents

egenn

rtemisbio:rtemis Bio-informatics

Bio-informatics utilities

Maintained by E.D. Gennatas. Last updated 5 months ago.

1.8 match 1 stars 2.60 score 1 scripts