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paws-r

paws.mobile:'Amazon Web Services' Mobile Services

Interface to 'Amazon Web Services' mobile services, including the 'Amplify' library for mobile applications, 'AppSync' back-end for mobile applications, and more <https://aws.amazon.com/>.

Maintained by Dyfan Jones. Last updated 17 days ago.

awsaws-sdk

332 stars 6.90 score

emitanaka

nestr:Build Nesting or Hierarchical Structures

Facilitates building a nesting or hierarchical structure as a list or data frame by using a human friendly syntax.

Maintained by Emi Tanaka. Last updated 3 years ago.

13 stars 4.37 score 12 scripts 1 dependents

cran

sensitivitymv:Sensitivity Analysis in Observational Studies

The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean. The main function in the package is senmv(), but amplify() and truncatedP() are also useful. The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.

Maintained by Paul R. Rosenbaum. Last updated 7 years ago.

2.08 score 4 dependents

cran

sensitivitymult:Sensitivity Analysis for Observational Studies with Multiple Outcomes

Sensitivity analysis for multiple outcomes in observational studies. For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) <doi:10.1214/16-AOAS942>. Alternatively, attention may focus on a few principal components in the principal() function. The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.

Maintained by Paul R. Rosenbaum. Last updated 8 years ago.

1.95 score 3 dependents

cran

fugue:Sensitivity Analysis Optimized for Matched Sets of Varied Sizes

As in music, a fugue statistic repeats a theme in small variations. Here, the psi-function that defines an m-statistic is slightly altered to maintain the same design sensitivity in matched sets of different sizes. The main functions in the package are sen() and senCI(). For sensitivity analyses for m-statistics, see Rosenbaum (2007) Biometrics 63 456-464 <doi:10.1111/j.1541-0420.2006.00717.x>.

Maintained by Paul R. Rosenbaum. Last updated 6 years ago.

1.70 score

cran

weightedRank:Sensitivity Analysis Using Weighted Rank Statistics

Performs a sensitivity analysis using weighted rank tests in observational studies with I blocks of size J; see Rosenbaum (2024) <doi:10.1080/01621459.2023.2221402>. The package can perform adaptive inference in block designs; see Rosenbaum (2012) <doi:10.1093/biomet/ass032>. The main functions are wgtRank(), wgtRankCI() and wgtRanktt().

Maintained by Paul Rosenbaum. Last updated 9 months ago.

1.00 score

cran

iTOS:Methods and Examples from Introduction to the Theory of Observational Studies

Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies." Data sets are 'aHDL' from Rosenbaum (2023a) <doi:10.1111/biom.13558> and 'bingeM' from Rosenbaum (2023b) <doi:10.1111/biom.13921>. The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data 'bingeM' from 'binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>). The book makes use of two other R packages, 'weightedRank' and 'tightenBlock'.

Maintained by Paul R. Rosenbaum. Last updated 7 months ago.

1.00 score

cran

dstat:Conditional Sensitivity Analysis for Matched Observational Studies

A d-statistic tests the null hypothesis of no treatment effect in a matched, nonrandomized study of the effects caused by treatments. A d-statistic focuses on subsets of matched pairs that demonstrate insensitivity to unmeasured bias in such an observational study, correcting for double-use of the data by conditional inference. This conditional inference can, in favorable circumstances, substantially increase the power of a sensitivity analysis (Rosenbaum (2010) <doi:10.1007/978-1-4419-1213-8_14>). There are two examples, one concerning unemployment from Lalive et al. (2006) <doi:10.1111/j.1467-937X.2006.00406.x>, the other concerning smoking and periodontal disease from Rosenbaum (2017) <doi:10.1214/17-STS621>.

Maintained by Paul R. Rosenbaum. Last updated 6 years ago.

1.00 score

cran

submax:Effect Modification in Observational Studies Using the Submax Method

Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates. The submax or subgroup-maximum method of Lee et al. (2017) <arXiv:1702.00525> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.

Maintained by Paul R. Rosenbaum. Last updated 7 years ago.

1.00 score

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