Showing 200 of total 2429 results (show query)

heliosdrm

pwr:Basic Functions for Power Analysis

Power analysis functions along the lines of Cohen (1988).

Maintained by Helios De Rosario. Last updated 1 years ago.

17.8 match 105 stars 12.97 score 2.6k scripts 28 dependents

bioc

igvR:igvR: integrative genomics viewer

Access to igv.js, the Integrative Genomics Viewer running in a web browser.

Maintained by Arkadiusz Gladki. Last updated 5 months ago.

visualizationthirdpartyclientgenomebrowsers

26.0 match 43 stars 8.31 score 118 scripts

psirusteam

samplesize4surveys:Sample Size Calculations for Complex Surveys

Computes the required sample size for estimation of totals, means and proportions under complex sampling designs.

Maintained by Hugo Andres Gutierrez Rojas. Last updated 5 years ago.

40.4 match 2 stars 4.78 score 60 scripts

alanarnholt

BSDA:Basic Statistics and Data Analysis

Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.

Maintained by Alan T. Arnholt. Last updated 2 years ago.

15.9 match 7 stars 9.11 score 1.3k scripts 6 dependents

ramnathv

htmlwidgets:HTML Widgets for R

A framework for creating HTML widgets that render in various contexts including the R console, 'R Markdown' documents, and 'Shiny' web applications.

Maintained by Carson Sievert. Last updated 1 years ago.

6.5 match 791 stars 19.05 score 7.4k scripts 3.1k dependents

cran

sae:Small Area Estimation

Functions for small area estimation.

Maintained by Yolanda Marhuenda. Last updated 5 years ago.

20.2 match 6 stars 5.49 score 83 scripts 8 dependents

datalorax

esvis:Visualization and Estimation of Effect Sizes

A variety of methods are provided to estimate and visualize distributional differences in terms of effect sizes. Particular emphasis is upon evaluating differences between two or more distributions across the entire scale, rather than at a single point (e.g., differences in means). For example, Probability-Probability (PP) plots display the difference between two or more distributions, matched by their empirical CDFs (see Ho and Reardon, 2012; <doi:10.3102/1076998611411918>), allowing for examinations of where on the scale distributional differences are largest or smallest. The area under the PP curve (AUC) is an effect-size metric, corresponding to the probability that a randomly selected observation from the x-axis distribution will have a higher value than a randomly selected observation from the y-axis distribution. Binned effect size plots are also available, in which the distributions are split into bins (set by the user) and separate effect sizes (Cohen's d) are produced for each bin - again providing a means to evaluate the consistency (or lack thereof) of the difference between two or more distributions at different points on the scale. Evaluation of empirical CDFs is also provided, with built-in arguments for providing annotations to help evaluate distributional differences at specific points (e.g., semi-transparent shading). All function take a consistent argument structure. Calculation of specific effect sizes is also possible. The following effect sizes are estimable: (a) Cohen's d, (b) Hedges' g, (c) percentage above a cut, (d) transformed (normalized) percentage above a cut, (e) area under the PP curve, and (f) the V statistic (see Ho, 2009; <doi:10.3102/1076998609332755>), which essentially transforms the area under the curve to standard deviation units. By default, effect sizes are calculated for all possible pairwise comparisons, but a reference group (distribution) can be specified.

Maintained by Daniel Anderson. Last updated 5 years ago.

visualization

17.9 match 51 stars 5.43 score 53 scripts

gnelson12

fishmethods:Fishery Science Methods and Models

Functions for applying a wide range of fisheries stock assessment methods.

Maintained by Gary A. Nelson. Last updated 1 months ago.

20.8 match 5 stars 4.12 score 136 scripts 1 dependents

kaifenglu

lrstat:Power and Sample Size Calculation for Non-Proportional Hazards and Beyond

Performs power and sample size calculation for non-proportional hazards model using the Fleming-Harrington family of weighted log-rank tests. The sequentially calculated log-rank test score statistics are assumed to have independent increments as characterized in Anastasios A. Tsiatis (1982) <doi:10.1080/01621459.1982.10477898>. The mean and variance of log-rank test score statistics are calculated based on Kaifeng Lu (2021) <doi:10.1002/pst.2069>. The boundary crossing probabilities are calculated using the recursive integration algorithm described in Christopher Jennison and Bruce W. Turnbull (2000, ISBN:0849303168). The package can also be used for continuous, binary, and count data. For continuous data, it can handle missing data through mixed-model for repeated measures (MMRM). In crossover designs, it can estimate direct treatment effects while accounting for carryover effects. For binary data, it can design Simon's 2-stage, modified toxicity probability-2 (mTPI-2), and Bayesian optimal interval (BOIN) trials. For count data, it can design group sequential trials for negative binomial endpoints with censoring. Additionally, it facilitates group sequential equivalence trials for all supported data types. Moreover, it can design adaptive group sequential trials for changes in sample size, error spending function, number and spacing or future looks. Finally, it offers various options for adjusted p-values, including graphical and gatekeeping procedures.

Maintained by Kaifeng Lu. Last updated 3 months ago.

cpp

14.7 match 2 stars 5.58 score 30 scripts

mjlajeunesse

metagear:Comprehensive Research Synthesis Tools for Systematic Reviews and Meta-Analysis

Functionalities for facilitating systematic reviews, data extractions, and meta-analyses. It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter- reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; figure and image extractions from PDFs; web scraping of citations; automated and manual data extraction from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges' d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software (i.e., metawin). Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution 7, 323-330 <doi:10.1111/2041-210X.12472>.

Maintained by Marc J. Lajeunesse. Last updated 4 years ago.

11.1 match 14 stars 6.71 score 91 scripts

r-forge

distr:Object Oriented Implementation of Distributions

S4-classes and methods for distributions.

Maintained by Peter Ruckdeschel. Last updated 2 months ago.

8.1 match 8.84 score 327 scripts 32 dependents

m-py

anticlust:Subset Partitioning via Anticlustering

The method of anticlustering partitions a pool of elements into groups (i.e., anticlusters) with the goal of maximizing between-group similarity or within-group heterogeneity. The anticlustering approach thereby reverses the logic of cluster analysis that strives for high within-group homogeneity and clear separation between groups. Computationally, anticlustering is accomplished by maximizing instead of minimizing a clustering objective function, such as the intra-cluster variance (used in k-means clustering) or the sum of pairwise distances within clusters. The main function anticlustering() gives access to optimal and heuristic anticlustering methods described in Papenberg and Klau (2021; <doi:10.1037/met0000301>), Brusco et al. (2020; <doi:10.1111/bmsp.12186>), Papenberg (2024; <doi:10.1111/bmsp.12315>), and Papenberg et al. (2025; <doi:10.1101/2025.03.03.641320>). The optimal algorithms require that an integer linear programming solver is installed. This package will install 'lpSolve' (<https://cran.r-project.org/package=lpSolve>) as a default solver, but it is also possible to use the package 'Rglpk' (<https://cran.r-project.org/package=Rglpk>), which requires the GNU linear programming kit (<https://www.gnu.org/software/glpk/glpk.html>), the package 'Rsymphony' (<https://cran.r-project.org/package=Rsymphony>), which requires the SYMPHONY ILP solver (<https://github.com/coin-or/SYMPHONY>), or the commercial solver Gurobi, which provides its own R package that is not available via CRAN (<https://www.gurobi.com/downloads/>). 'Rglpk', 'Rsymphony', 'gurobi' and their system dependencies have to be manually installed by the user because they are only suggested dependencies. Full access to the bicriterion anticlustering method proposed by Brusco et al. (2020) is given via the function bicriterion_anticlustering(), while kplus_anticlustering() implements the full functionality of the k-plus anticlustering approach proposed by Papenberg (2024). Some other functions are available to solve classical clustering problems. The function balanced_clustering() applies a cluster analysis under size constraints, i.e., creates equal-sized clusters. The function matching() can be used for (unrestricted, bipartite, or K-partite) matching. The function wce() can be used optimally solve the (weighted) cluster editing problem, also known as correlation clustering, clique partitioning problem or transitivity clustering.

Maintained by Martin Papenberg. Last updated 4 hours ago.

6.1 match 34 stars 9.25 score 60 scripts 2 dependents

sbgraves237

Ecdat:Data Sets for Econometrics

Data sets for econometrics, including political science.

Maintained by Spencer Graves. Last updated 4 months ago.

7.3 match 2 stars 7.25 score 740 scripts 3 dependents

samhforbes

PupillometryR:A Unified Pipeline for Pupillometry Data

Provides a unified pipeline to clean, prepare, plot, and run basic analyses on pupillometry experiments.

Maintained by Samuel Forbes. Last updated 1 years ago.

6.9 match 44 stars 7.58 score 288 scripts 1 dependents

arsilva87

soilphysics:Soil Physical Analysis

Basic and model-based soil physical analyses.

Maintained by Anderson Rodrigo da Silva. Last updated 3 years ago.

10.4 match 11 stars 4.83 score 12 scripts

myaseen208

PakPC2023:Pakistan Population Census 2023

Provides data sets and functions for exploration of Pakistan Population Census 2023 (<https://www.pbs.gov.pk/>).

Maintained by Muhammad Yaseen. Last updated 5 months ago.

12.0 match 1 stars 4.18 score 2 scripts 1 dependents

edzer

intervals:Tools for Working with Points and Intervals

Tools for working with and comparing sets of points and intervals.

Maintained by Edzer Pebesma. Last updated 7 months ago.

cpp

5.3 match 11 stars 9.40 score 122 scripts 90 dependents