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imbi-heidelberg

DescrTab2:Publication Quality Descriptive Statistics Tables

Provides functions to create descriptive statistics tables for continuous and categorical variables. By default, summary statistics such as mean, standard deviation, quantiles, minimum and maximum for continuous variables and relative and absolute frequencies for categorical variables are calculated. 'DescrTab2' features a sophisticated algorithm to choose appropriate test statistics for your data and provides p-values. On top of this, confidence intervals for group differences of appropriated summary measures are automatically produces for two-group comparison. Tables generated by 'DescrTab2' can be integrated in a variety of document formats, including .html, .tex and .docx documents. 'DescrTab2' also allows printing tables to console and saving table objects for later use.

Maintained by Jan Meis. Last updated 1 years ago.

categorical-variablescontinuous-variabledescriptive-statisticsp-valuesstatistical-testsstatistics

9 stars 6.71 score 19 scripts 1 dependents

cran

exact2x2:Exact Tests and Confidence Intervals for 2x2 Tables

Calculates conditional exact tests (Fisher's exact test, Blaker's exact test, or exact McNemar's test) and unconditional exact tests (including score-based tests on differences in proportions, ratios of proportions, and odds ratios, and Boshcloo's test) with appropriate matching confidence intervals, and provides power and sample size calculations. Gives melded confidence intervals for the binomial case (Fay, et al, 2015, <DOI:10.1111/biom.12231>). Gives boundary-optimized rejection region test (Gabriel, et al, 2018, <DOI:10.1002/sim.7579>), an unconditional exact test for the situation where the controls are all expected to fail. Gives confidence intervals compatible with exact McNemar's or sign tests (Fay and Lumbard, 2021, <DOI:10.1002/sim.8829>). For review of these kinds of exact tests see Fay and Hunsberger (2021, <DOI:10.1214/21-SS131>).

Maintained by Michael P. Fay. Last updated 1 years ago.

3 stars 5.81 score 6 dependents

matteo21q

dani:Design and Analysis of Non-Inferiority Trials

Provides tools to help with the design and analysis of non-inferiority trials. These include functions for doing sample size calculations and for analysing non-inferiority trials, using a variety of outcome types and population-level sumamry measures. It also features functions to make trials more resilient by using the concept of non-inferiority frontiers, as described in Quartagno et al. (2019) <arXiv:1905.00241>. Finally it includes function to design and analyse MAMS-ROCI (aka DURATIONS) trials.

Maintained by Matteo Quartagno. Last updated 7 months ago.

2 stars 5.33 score 27 scripts

cran

exactci:Exact P-Values and Matching Confidence Intervals for Simple Discrete Parametric Cases

Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases (see Fay (2010) <doi:10.32614/rj-2010-008>).

Maintained by Michael P. Fay. Last updated 2 years ago.

5.00 score 10 dependents

jkurle

robust2sls:Outlier Robust Two-Stage Least Squares Inference and Testing

An implementation of easy tools for outlier robust inference in two-stage least squares (2SLS) models. The user specifies a reference distribution against which observations are classified as outliers or not. After removing the outliers, adjusted standard errors are automatically provided. Furthermore, several statistical tests for the false outlier detection rate can be calculated. The outlier removing algorithm can be iterated a fixed number of times or until the procedure converges. The algorithms and robust inference are described in more detail in Jiao (2019) <https://drive.google.com/file/d/1qPxDJnLlzLqdk94X9wwVASptf1MPpI2w/view>.

Maintained by Jonas Kurle. Last updated 2 years ago.

1 stars 4.43 score 18 scripts

alfodefalco

dPCP:Automated Analysis of Multiplex Digital PCR Data

The automated clustering and quantification of the digital PCR data is based on the combination of 'DBSCAN' (Hahsler et al. (2019) <doi:10.18637/jss.v091.i01>) and 'c-means' (Bezdek et al. (1981) <doi:10.1007/978-1-4757-0450-1>) algorithms. The analysis is independent of multiplexing geometry, dPCR system, and input amount. The details about input data and parameters are available in the vignette.

Maintained by Alfonso De Falco. Last updated 2 years ago.

2 stars 4.36 score 23 scripts

vusaverse

vvdoctor:Statistical Test App with R 'shiny'

Provides a user-friendly R 'shiny' app for performing various statistical tests on datasets. It allows users to upload data in numerous formats and perform statistical analyses. The app dynamically adapts its options based on the selected columns and supports both single and multiple column comparisons. The app's user interface is designed to streamline the process of selecting datasets, columns, and test options, making it easy for users to explore and interpret their data. The underlying functions for statistical tests are well-organized and can be used independently within other R scripts.

Maintained by Tomer Iwan. Last updated 11 months ago.

hypothesis-testingr-r-shinyshiny-appsshiny-rstatistical-testsstatisticsstats

7 stars 4.24 score 3 scripts

mnelsonr

dsrTest:Tests and Confidence Intervals on Directly Standardized Rates for Several Methods

Perform a test of a simple null hypothesis about a directly standardized rate and obtain the matching confidence interval using a choice of methods.

Maintained by Michael Nelson. Last updated 3 years ago.

3.70 score 7 scripts

cran

asht:Applied Statistical Hypothesis Tests

Gives some hypothesis test functions (sign test, median and other quantile tests, Wilcoxon signed rank test, coefficient of variation test, test of normal variance, test on weighted sums of Poisson [see Fay and Kim <doi:10.1002/bimj.201600111>], sample size for t-tests with different variances and non-equal n per arm, Behrens-Fisher test, nonparametric ABC intervals, Wilcoxon-Mann-Whitney test [with effect estimates and confidence intervals, see Fay and Malinovsky <doi:10.1002/sim.7890>], two-sample melding tests [see Fay, Proschan, and Brittain <doi:10.1111/biom.12231>], one-way ANOVA allowing var.equal=FALSE [see Brown and Forsythe, 1974, Biometrics]), prevalence confidence intervals that adjust for sensitivity and specificity [see Lang and Reiczigel, 2014 <doi:10.1016/j.prevetmed.2013.09.015>] or Bayer, Fay, and Graubard, 2023 <doi:10.48550/arXiv.2205.13494>). The focus is on hypothesis tests that have compatible confidence intervals, but some functions only have confidence intervals (e.g., prevSeSp).

Maintained by Michael P. Fay. Last updated 2 years ago.

1.99 score 1 dependents

manuelaott

pCalibrate:Bayesian Calibrations of p-Values

Implements transformations of p-values to the smallest possible Bayes factor within the specified class of alternative hypotheses, as described in Held & Ott (2018, <doi:10.1146/annurev-statistics-031017-100307>). Covers several common testing scenarios such as z-tests, t-tests, likelihood ratio tests and the F-test.

Maintained by Manuela Ott. Last updated 5 years ago.

1.36 score 23 scripts

lafaye

rPowerSampleSize:Sample Size Computations Controlling the Type-II Generalized Family-Wise Error Rate

The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) <doi:10.1080/10543406.2013.860156>.

Maintained by Pierre Lafaye de Micheaux. Last updated 7 years ago.

1.00 score 9 scripts

cran

nivm:Noninferiority Tests with Variable Margins

Noninferiority tests for difference in failure rates at a prespecified control rate or prespecified time. For details, see Fay and Follmann, 2016 <DOI:10.1177/1740774516654861>.

Maintained by Michael P. Fay. Last updated 1 years ago.

1.00 score

cran

csci:Current Status Confidence Intervals

Calculates pointwise confidence intervals for the cumulative distribution function of the event time for current status data, data where each individual is assessed at one time to see if they had the event or not by the assessment time.

Maintained by Michael P. Fay. Last updated 4 years ago.

1.00 score

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