R-universe search: exports:logrank

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harrelfe

Hmisc:Harrell Miscellaneous

Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.

Maintained by Frank E Harrell Jr. Last updated 5 days ago.

fortran

209 stars 17.64 score 17k scripts 750 dependents

goranbrostrom

eha:Event History Analysis

Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. Parametric accelerated failure time models for left truncated and right censored data. Proportional hazards models for tabular and register data. Sampling of risk sets in Cox regression, selections in the Lexis diagram, bootstrapping. Broström (2022) <doi:10.1201/9780429503764>.

Maintained by Göran Broström. Last updated 10 months ago.

fortran openblas

7 stars 9.76 score 308 scripts 10 dependents

imbs-hl

survivalsvm:Survival Support Vector Analysis

Performs support vectors analysis for data sets with survival outcome. Three approaches are available in the package: The regression approach takes censoring into account when formulating the inequality constraints of the support vector problem. In the ranking approach, the inequality constraints set the objective to maximize the concordance index for comparable pairs of observations. The hybrid approach combines the regression and ranking constraints in the same model.

Maintained by Cesaire Fouodo. Last updated 7 years ago.

16 stars 5.17 score 61 scripts 1 dependents

neilhspencer

ANSM5:Functions and Data for the Book "Applied Nonparametric Statistical Methods", 5th Edition

Functions and data to accompany the 5th edition of the book "Applied Nonparametric Statistical Methods" (4th edition: Sprent & Smeeton, 2024, ISBN:158488701X), the revisions from the 4th edition including a move from describing the output from a miscellany of statistical software packages to using R. While the output from many of the functions can also be obtained using a range of other R functions, this package provides functions in a unified setting and give output using both p-values and confidence intervals, exemplifying the book's approach of treating p-values as a guide to statistical importance and not an end product in their own right. Please note that in creating the ANSM5 package we do not claim to have produced software which is necessarily the most computationally efficient nor the most comprehensive.

Maintained by Neil Spencer. Last updated 7 months ago.

3.78 score 4 scripts

yikeshu0611

cutoff:Seek the Significant Cutoff Value

Seek the significant cutoff value for a continuous variable, which will be transformed into a classification, for linear regression, logistic regression, logrank analysis and cox regression. First of all, all combinations will be gotten by combn() function. Then n.per argument, abbreviated of total number percentage, will be used to remove the combination of smaller data group. In logistic, Cox regression and logrank analysis, we will also use p.per argument, patient percentage, to filter the lower proportion of patients in each group. Finally, p value in regression results will be used to get the significant combinations and output relevant parameters. In this package, there is no limit to the number of cutoff points, which can be 1, 2, 3 or more. Missing values will be deleted by na.omit() function before analysis.

Maintained by Jing Zhang. Last updated 5 years ago.

1 stars 3.67 score 31 scripts 1 dependents