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r-lib

pak:Another Approach to Package Installation

The goal of 'pak' is to make package installation faster and more reliable. In particular, it performs all HTTP operations in parallel, so metadata resolution and package downloads are fast. Metadata and package files are cached on the local disk as well. 'pak' has a dependency solver, so it finds version conflicts before performing the installation. This version of 'pak' supports CRAN, 'Bioconductor' and 'GitHub' packages as well.

Maintained by Gábor Csárdi. Last updated 11 hours ago.

73.3 match 717 stars 13.05 score 277 scripts 17 dependents

dfsp-spirit

wal:Read and Write 'wal' Bitmap Image Files and Other 'Quake' Assets

Read 'Quake' assets including bitmap images and textures in 'wal' file format. This package also provides support for extracting these assets from 'WAD' and 'PAK' file archives. It can also read models in 'MDL' and 'MD2' formats.

Maintained by Tim Schäfer. Last updated 1 years ago.

imagepakquakequake-enginequake2textureswadwal

19.7 match 9 stars 4.65 score 1 scripts

predictiveecology

Require:Installing and Loading R Packages for Reproducible Workflows

A single key function, 'Require' that makes rerun-tolerant versions of 'install.packages' and `require` for CRAN packages, packages no longer on CRAN (i.e., archived), specific versions of packages, and GitHub packages. This approach is developed to create reproducible workflows that are flexible and fast enough to use while in development stages, while able to build snapshots once a stable package collection is found. As with other functions in a reproducible workflow, this package emphasizes functions that return the same result whether it is the first or subsequent times running the function, with subsequent times being sufficiently fast that they can be run every time without undue waiting burden on the user or developer.

Maintained by Eliot J B McIntire. Last updated 15 days ago.

5.4 match 22 stars 9.42 score 144 scripts 13 dependents

ichcha-m

cophescan:Adaptation of the Coloc Method for PheWAS

A Bayesian method for Phenome-wide association studies (PheWAS) that identifies causal associations between genetic variants and traits, while simultaneously addressing confounding due to linkage disequilibrium. For details see Manipur et al (2023) <doi:10.1101/2023.06.29.546856>.

Maintained by Ichcha Manipur. Last updated 9 months ago.

cppopenmp

5.2 match 6 stars 5.76 score 24 scripts

pakdaewo

ALassoSurvIC:Adaptive Lasso for the Cox Regression with Interval Censored and Possibly Left Truncated Data

Penalized variable selection tools for the Cox proportional hazards model with interval censored and possibly left truncated data. It performs variable selection via penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty. The optimal thresholding parameter can be searched by the package based on the profile Bayesian information criterion (BIC). The asymptotic validity of the methodology is established in Li et al. (2019 <doi:10.1177/0962280219856238>). The unpenalized nonparametric maximum likelihood estimation for interval censored and possibly left truncated data is also available.

Maintained by Daewoo Pak. Last updated 2 years ago.

cpp

9.3 match 2 stars 2.15 score 14 scripts

pakdaewo

YPInterimTesting:Interim Monitoring Using Adaptively Weighted Log-Rank Test in Clinical Trials

For any spending function specified by the user, this package provides corresponding boundaries for interim testing using the adaptively weighted log-rank test developed by Yang and Prentice (2010 <doi:10.1111/j.1541-0420.2009.01243.x>). The package uses a re-sampling method to obtain stopping boundaries at the interim looks.The output consists of stopping boundaries and observed values of the test statistics at the interim looks, along with nominal p-values defined as the probability of the test exceeding the specific observed test statistic value or critical value, regardless of the test behavior at other looks. The asymptotic validity of the stopping boundaries is established in Yang (2018 <doi:10.1002/sim.7958>).

Maintained by Daewoo Pak. Last updated 6 years ago.

cpp

9.4 match 1.70 score 6 scripts

pakdaewo

ClinicalTrialSummary:Summary Measures for Clinical Trials with Survival Outcomes

Provides estimates of several summary measures for clinical trials including the average hazard ratio, the weighted average hazard ratio, the restricted superiority probability ratio, the restricted mean survival difference and the ratio of restricted mean times lost, based on the short-term and long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) which accommodates various non-proportional hazards scenarios. The inference procedures and the asymptotic results for the summary measures are discussed in Yang (2018, <doi:10.1002/sim.7676>).

Maintained by Daewoo Pak. Last updated 6 years ago.

cpp

9.4 match 1.70 score 10 scripts

daewoopak

EventWinRatios:Event-Specific Win Ratios for Terminal and Non-Terminal Events

Provides several confidence interval and testing procedures using event-specific win ratios for semi-competing risks data with non-terminal and terminal events, as developed in Yang et al. (2021<doi:10.1002/sim.9266>). Compared with conventional methods for survival data, these procedures are designed to utilize more data for improved inference procedures with semi-competing risks data. The event-specific win ratios were introduced in Yang and Troendle (2021<doi:10.1177/1740774520972408>). In this package, the event-specific win ratios and confidence intervals are obtained for each event type, and several testing procedures are developed for the global null of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazard assumptions, under which the event-specific win ratios converge to the hazard ratios, and a test of equal hazard ratios are provided. For summarizing the treatment effect on all events, confidence intervals for linear combinations of the event-specific win ratios are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al (2021<doi:10.1002/sim.9266>). Also, transformations are used to yield better control of the type one error rates for moderately sized data sets.

Maintained by Daewoo Pak. Last updated 3 years ago.

9.2 match 1.70 score 4 scripts

daewoopak

YPmodelPhreg:The Short-Term and Long-Term Hazard Ratio Model with Proportional Adjustment

Provides covariate-adjusted comparison of two groups of right censored data, where the binary group variable has separate short-term and long-term effects on the hazard function, while effects of covariates such as age, blood pressure, etc. are proportional on the hazard. The model was studied in Yang and Prentice (2015) <doi:10.1002/sim.6453> and it extends the two sample version of the short-term and long-term hazard ratio model proposed in Yang and Prentice (2005) <doi:10.1093/biomet/92.1.1>. The model extends the usual Cox proportional hazards model to allow more flexible hazard ratio patterns, such as gradual onset of effect, diminishing effect, and crossing hazard or survival functions. This package provides the following: 1) point estimates and confidence intervals for model parameters; 2) point estimate and confidence interval of the average hazard ratio; and 3) plots of estimated hazard ratio function with point-wise and simultaneous confidence bands.

Maintained by Daewoo Pak. Last updated 2 years ago.

9.2 match 1.70 score 5 scripts

pablo-najera

cdmTools:Useful Tools for Cognitive Diagnosis Modeling

Provides useful tools for cognitive diagnosis modeling (CDM). The package includes functions for empirical Q-matrix estimation and validation, such as the Hull method (Nájera, Sorrel, de la Torre, & Abad, 2021, <doi:10.1111/bmsp.12228>) and the discrete factor loading method (Wang, Song, & Ding, 2018, <doi:10.1007/978-3-319-77249-3_29>). It also contains dimensionality assessment procedures for CDM, including parallel analysis and automated fit comparison as explored in Nájera, Abad, and Sorrel (2021, <doi:10.3389/fpsyg.2021.614470>). Other relevant methods and features for CDM applications, such as the restricted DINA model (Nájera et al., 2023; <doi:10.3102/10769986231158829>), the general nonparametric classification method (Chiu et al., 2018; <doi:10.1007/s11336-017-9595-4>), and corrected estimation of the classification accuracy via multiple imputation (Kreitchmann et al., 2022; <doi:10.3758/s13428-022-01967-5>) are also available. Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices.

Maintained by Pablo Nájera. Last updated 1 years ago.

3.3 match 3 stars 3.88 score 17 scripts 1 dependents

jimbrig

configR:R Conviguration Package

Setup and configure an optimal R development environment.

Maintained by Jimmy Briggs. Last updated 3 years ago.

2.3 match 1 stars 3.11 score 260 scripts

r-lib

pkgdepends:Package Dependency Resolution and Downloads

Find recursive dependencies of 'R' packages from various sources. Solve the dependencies to obtain a consistent set of packages to install. Download packages, and install them. It supports packages on 'CRAN', 'Bioconductor' and other 'CRAN-like' repositories, 'GitHub', package 'URLs', and local package trees and files. It caches metadata and package files via the 'pkgcache' package, and performs all 'HTTP' requests, downloads, builds and installations in parallel. 'pkgdepends' is the workhorse of the 'pak' package.

Maintained by Gábor Csárdi. Last updated 17 days ago.

0.5 match 110 stars 9.89 score 67 scripts 5 dependents

bojianzhu

Rlibkdv:A Versatile Kernel Density Visualization Library for Geospatial Analytics (Heatmap)

Unlock the power of large-scale geospatial analysis, quickly generate high-resolution kernel density visualizations, supporting advanced analysis tasks such as bandwidth-tuning and spatiotemporal analysis. Regardless of the size of your dataset, our library delivers efficient and accurate results. Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu, Reynold Cheng (2023) <doi:10.1145/3555041.3589401>. Tsz Nam Chan, Rui Zang, Pak Lon Ip, Leong Hou U, Jianliang Xu (2023) <doi:10.1145/3555041.3589711>. Tsz Nam Chan, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.1145/3514221.3517823>. Tsz Nam Chan, Pak Lon Ip, Kaiyan Zhao, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.14778/3554821.3554855>. Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.14778/3503585.3503591>. Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Byron Choi, Jianliang Xu (2022) <doi:10.14778/3494124.3494135>. Tsz Nam Chan, Pak Lon Ip, Leong Hou U, Weng Hou Tong, Shivansh Mittal, Ye Li, Reynold Cheng (2021) <doi:10.14778/3476311.3476312>. Tsz Nam Chan, Zhe Li, Leong Hou U, Jianliang Xu, Reynold Cheng (2021) <doi:10.14778/3461535.3461540>. Tsz Nam Chan, Reynold Cheng, Man Lung Yiu (2020) <doi:10.1145/3318464.3380561>. Tsz Nam Chan, Leong Hou U, Reynold Cheng, Man Lung Yiu, Shivansh Mittal (2020) <doi:10.1109/TKDE.2020.3018376>. Tsz Nam Chan, Man Lung Yiu, Leong Hou U (2019) <doi:10.1109/ICDE.2019.00055>.

Maintained by Bojian Zhu. Last updated 1 years ago.

cpp

1.0 match 4 stars 4.30 score 3 scripts

iembry

install.load:Check, Install and Load CRAN Packages

The function 'install_load' checks the local R library(ies) to see if the required package(s) is/are installed or not. If the package(s) is/are not installed, then the package(s) will be installed along with the required dependency(ies). This function pulls source or binary packages from the Posit/RStudio-sponsored CRAN mirror. Lastly, the chosen package(s) is/are loaded. The function 'load_package' simply loads the provided package(s). If this package does not fit your needs, then you may want to consider these other R packages: 'needs', 'easypackages', 'pacman', 'pak', 'anyLib', and/or 'librarian'.

Maintained by Irucka Embry. Last updated 2 years ago.

0.5 match 3.86 score 146 scripts

paulesantos

iucnr:IUCN Red List Data

Facilitates access to the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, a comprehensive global inventory of species at risk of extinction. This package streamlines the process of determining conservation status by matching species names with Red List data, providing tools to easily query and retrieve conservation statuses. Designed to support biodiversity research and conservation planning, this package relies on data from the 'iucnrdata' package, available on GitHub <https://github.com/PaulESantos/iucnrdata>. To install the data package, use pak::pak('PaulESantos/iucnrdata').

Maintained by Paul Efren Santos Andrade. Last updated 4 months ago.

0.8 match 1.70 score

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