Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.
API method:
GET /api/packages?search=hello&page=1&limit=20
where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned
in response headers.
If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.
Bayesian logistic regression model with optional EXchangeability-NonEXchangeability parameter modelling for flexible borrowing from historical or concurrent data-sources. The safety model can guide dose-escalation decisions for adaptive oncology Phase I dose-escalation trials which involve an arbitrary number of drugs. Please refer to Neuenschwander et al. (2008) <doi:10.1002/sim.3230> and Neuenschwander et al. (2016) <doi:10.1080/19466315.2016.1174149> for details on the methodology.
Medication adherence, defined as medication-taking behavior that aligns with the agreed-upon treatment protocol, is critical for realizing the benefits of prescription medications. Medication adherence can be assessed using electronic adherence monitoring devices (EAMDs), pill bottles or boxes that contain a computer chip that records the date and time of each opening (or â actuationâ ). Before researchers can use EAMD data, they must apply a series of decision rules to transform actuation data into adherence data. The purpose of this R package ('oncmap') is to transform EAMD actuations in the form of a raw .csv file, information about the patient, regimen, and non-monitored periods into two daily adherence values -- Dose Taken and Correct Dose Taken.
Two-stage design for single-arm phase II trials with time-to-event endpoints (e.g., clinical trials on immunotherapies among cancer patients) can be calculated using this package. Two notable advantages of the package: 1) It provides flexible choices from three design methods (optimal, minmax, and admissible), and 2) the power of the design is more accurately calculated using the exact variance in the one-sample log-rank test. The package can be used for 1) planning the sample sizes and other design parameters, and 2) conducting the interim and final analyses for the Go/No-go decisions. More details about the design method can be found in: Wu, J, Chen L, Wei J, Weiss H, Chauhan A. (2020). <doi:10.1002/pst.1983>.
Plotting toolbox for 2D oceanographic data (satellite data, sea surface temperature, chlorophyll, ocean fronts & bathymetry). Recognized classes and formats include netcdf, Raster, .nc and .gz files.
An R wrapper for the OneMap.Sg API <https://www.onemap.gov.sg/docs/>. Functions help users query data from the API and return raw JSON data in "tidy" formats. Support is also available for users to retrieve data from multiple API calls and integrate results into single dataframes, without needing to clean and merge the data themselves. This package is best suited for users who would like to perform analyses with Singapore's spatial data without having to perform excessive data cleaning.
Evaluates the Owen Q-function for an integer value of the degrees of freedom, by applying Owen's algorithm (1965) <doi:10.1093/biomet/52.3-4.437>. It is useful for the calculation of the power of equivalence tests.
In the context of data fusion, the package provides a set of functions dedicated to the solving of recoding problems using optimal transportation theory (Gares, Guernec, Savy (2019) <doi:10.1515/ijb-2018-0106> and Gares, Omer (2020) <doi:10.1080/01621459.2020.1775615>). From two databases with no overlapping part except a subset of shared variables, the functions of the package assist users until obtaining a unique synthetic database, where the missing information is fully completed.
Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) <DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.
Maximum homogeneity clustering algorithm for one-dimensional data described in W. D. Fisher (1958) <doi:10.1080/01621459.1958.10501479> via dynamic programming.
Interface to OpenStreetMap API for fetching and saving data from/to the OpenStreetMap database (<https://wiki.openstreetmap.org/wiki/API_v0.6>).
Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). The package includes functions to perform independent component analysis - orthogonal signal deconvolution (ICA-OSD), independent component regression (ICR), multivariate curve resolution (MCR-ALS) and orthogonal signal deconvolution (OSD) alone.
Quaternions and Octonions are four- and eight- dimensional extensions of the complex numbers. They are normed division algebras over the real numbers and find applications in spatial rotations (quaternions), and string theory and relativity (octonions). The quaternions are noncommutative and the octonions nonassociative. See the package vignette for more details.
Enables the usage of the OpenDota API from <https://www.opendota.com/>, get game lists, and download JSON's of parsed replays from the OpenDota API. Also has functionality to execute own code to extract the specific parts of the JSON file.
Summarises key information in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Assess suitability to perform specific epidemiological studies and explore the different domains to obtain feasibility counts and trends.
The popular population genetic software Treemix by Pickrell and Pritchard (2012) <DOI:10.1371/journal.pgen.1002967> estimates the number of migration edges on a population tree. However, it can be difficult to determine the number of migration edges to include. Previously, it was customary to stop adding migration edges when 99.8% of variation in the data was explained, but OptM automates this process using an ad hoc statistic based on the second-order rate of change in the log likelihood. OptM also has added functionality for various threshold modeling to compare with the ad hoc statistic.
An RStudio addin to assist with removing objects from the global environment. Features include removing objects according to name patterns and object type. During the course of an analysis, temporary objects are often created and this tool assists with removing them quickly. This can be useful when memory management within R is important.
An unofficial wrapper for okx exchange v5 API <https://www.okx.com/docs-v5/en/>, including REST API and WebSocket API.
Optimal testing under general dependence. The R package implements procedures proposed in Wang, Han, and Tong (2022). The package includes parameter estimation procedures, the computation for the posterior probabilities, and the testing procedure.
Make querying on OData easier. It exposes an ODataQuery object that can be manipulated and provides features such as selection, filtering and ordering.
Calculates ordinated diet breadth with some plotting functions.
Two-part system for first collecting then managing direct observation data, as described by Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.
This package provides routines for finding an Optimal System of Distinct Representatives (OSDR), as defined by D.Gale (1968) <doi:10.1016/S0021-9800(68)80039-0>.
Several Oceanographic data sets are provided for use by the oce package and for other purposes.
The log-rank test is performed to assess the survival outcomes between two group. When there is no proper control group or obtaining such data is cumbersome, one sample log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003)<doi:10.1093/jnci/djt227> and variation of the test for small sample sizes which is detailed in FD Liddell (1984)<doi:10.1136/jech.38.1.85> paper. Visualization function in the package generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest.