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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.

17.0 match 7 stars 9.11 score 1.3k scripts 6 dependents

bnaras

homomorpheR:Homomorphic Computations in R

Homomorphic computations in R for privacy-preserving applications. Currently only the Paillier Scheme is implemented.

Maintained by Balasubramanian Narasimhan. Last updated 3 years ago.

24.2 match 4 stars 5.73 score 18 scripts 1 dependents

ropensci

ReLTER:An Interface for the eLTER Community

ReLTER provides access to DEIMS-SDR (https://deims.org/), and allows interaction with data and software implemented by eLTER Research Infrastructure (RI) thus improving data sharing among European LTER projects. ReLTER uses the R language to access and interact with the DEIMS-SDR archive of information shared by the Long Term Ecological Research (LTER) network. This package grew within eLTER H2020 as a major project that will help advance the development of European Long-Term Ecosystem Research Infrastructures (eLTER RI - https://elter-ri.eu). The ReLTER package functions in particular allow to: - retrieve the information about entities (e.g. sites, datasets, and activities) shared by DEIMS-SDR (see e.g. get_site_info function); - interact with the [ODSEurope](maps.opendatascience.eu) starting with the dataset shared by [DEIMS-SDR](https://deims.org/) (see e.g. [get_site_ODS](https://docs.ropensci.org/ReLTER/reference/get_site_ODS.html) function); - use the eLTER site informations to download and crop geospatial data from other platforms (see e.g. get_site_ODS function); - improve the quality of the dataset (see e.g. get_id_worms). Functions currently implemented are derived from discussions of the needs among the eLTER users community. The ReLTER package will continue to follow the progress of eLTER-RI and evolve, adding new tools and improvements as required.

Maintained by Alessandro Oggioni. Last updated 1 years ago.

biodiversity-informaticsdata-scienceecologyelterresearch-infrastructure

26.3 match 12 stars 3.38 score 4 scripts

topepo

caret:Classification and Regression Training

Misc functions for training and plotting classification and regression models.

Maintained by Max Kuhn. Last updated 3 months ago.

3.6 match 1.6k stars 19.24 score 61k scripts 303 dependents

elipousson

legistarapi:Access the Legistar Web API

Access public data using the Legistar Web API.

Maintained by Eli Pousson. Last updated 6 months ago.

legistar

25.7 match 2.48 score 3 scripts

zhaokg

Rbeast:Bayesian Change-Point Detection and Time Series Decomposition

Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.

Maintained by Kaiguang Zhao. Last updated 6 months ago.

anomoly-detectionbayesian-time-seriesbreakpoint-detectionchangepoint-detectioninterrupted-time-seriesseasonality-analysisstructural-breakpointtechnical-analysistime-seriestime-series-decompositiontrendtrend-analysis

6.9 match 302 stars 7.63 score 89 scripts

mrc-ide

eppasm:Age-structured EPP Model for HIV Epidemic Estimates

What the package does (one paragraph).

Maintained by Jeff Eaton. Last updated 3 months ago.

9.4 match 6 stars 5.04 score 34 scripts 3 dependents

dnychka

fields:Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

Maintained by Douglas Nychka. Last updated 9 months ago.

fortran

3.4 match 15 stars 12.60 score 7.7k scripts 295 dependents

mikejohnson51

climateR:climateR

Find, subset, and retrive geospatial data by AOI.

Maintained by Mike Johnson. Last updated 3 months ago.

aoiclimatedatasetgeospatialgridded-climate-dataweather

4.9 match 187 stars 8.74 score 156 scripts 1 dependents

agqhammond

UKFE:UK Flood Estimation

Functions to implement the methods of the Flood Estimation Handbook (FEH), associated updates and the revitalised flood hydrograph model (ReFH). Currently the package uses NRFA peak flow dataset version 13. Aside from FEH functionality, further hydrological functions are available. Most of the methods implemented in this package are described in one or more of the following: "Flood Estimation Handbook", Centre for Ecology & Hydrology (1999, ISBN:0 948540 94 X). "Flood Estimation Handbook Supplementary Report No. 1", Kjeldsen (2007, ISBN:0 903741 15 7). "Regional Frequency Analysis - an approach based on L-moments", Hosking & Wallis (1997, ISBN: 978 0 521 01940 8). "Proposal of the extreme rank plot for extreme value analysis: with an emphasis on flood frequency studies", Hammond (2019, <doi:10.2166/nh.2019.157>). "Making better use of local data in flood frequency estimation", Environment Agency (2017, ISBN: 978 1 84911 387 8). "Sampling uncertainty of UK design flood estimation" , Hammond (2021, <doi:10.2166/nh.2021.059>). "Improving the FEH statistical procedures for flood frequency estimation", Environment Agency (2008, ISBN: 978 1 84432 920 5). "Low flow estimation in the United Kingdom", Institute of Hydrology (1992, ISBN 0 948540 45 1). Wallingford HydroSolutions, (2016, <http://software.hydrosolutions.co.uk/winfap4/Urban-Adjustment-Procedure-Technical-Note.pdf>). Data from the UK National River Flow Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions: <https://nrfa.ceh.ac.uk/costs-terms-and-conditions>).

Maintained by Anthony Hammond. Last updated 1 months ago.

23.5 match 1 stars 1.78 score

eco-hydro

phenofit:Extract Remote Sensing Vegetation Phenology

The merits of 'TIMESAT' and 'phenopix' are adopted. Besides, a simple and growing season dividing method and a practical snow elimination method based on Whittaker were proposed. 7 curve fitting methods and 4 phenology extraction methods were provided. Parameters boundary are considered for every curve fitting methods according to their ecological meaning. And 'optimx' is used to select best optimization method for different curve fitting methods. Reference: Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction package, phenofit version 0.3.1, <doi:10.5281/zenodo.5150204>; Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. <doi:10.1029/2020JG005636>; Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13–24; Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). Agric. For. Meteorol. 248, 408–417. <doi:10.1016/j.agrformet.2017.10.026>.

Maintained by Dongdong Kong. Last updated 1 months ago.

phenologyremote-sensingopenblascppopenmp

4.6 match 78 stars 7.71 score 332 scripts

wraff

wrMisc:Analyze Experimental High-Throughput (Omics) Data

The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples. Many times large experimental datasets need some additional filtering, adequate functions are provided. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too. Batch reading (or writing) of sets of files and combining data to arrays is supported, too.

Maintained by Wolfgang Raffelsberger. Last updated 7 months ago.

7.7 match 4.44 score 33 scripts 4 dependents

bodkan

slendr:A Simulation Framework for Spatiotemporal Population Genetics

A framework for simulating spatially explicit genomic data which leverages real cartographic information for programmatic and visual encoding of spatiotemporal population dynamics on real geographic landscapes. Population genetic models are then automatically executed by the 'SLiM' software by Haller et al. (2019) <doi:10.1093/molbev/msy228> behind the scenes, using a custom built-in simulation 'SLiM' script. Additionally, fully abstract spatial models not tied to a specific geographic location are supported, and users can also simulate data from standard, non-spatial, random-mating models. These can be simulated either with the 'SLiM' built-in back-end script, or using an efficient coalescent population genetics simulator 'msprime' by Baumdicker et al. (2022) <doi:10.1093/genetics/iyab229> with a custom-built 'Python' script bundled with the R package. Simulated genomic data is saved in a tree-sequence format and can be loaded, manipulated, and summarised using tree-sequence functionality via an R interface to the 'Python' module 'tskit' by Kelleher et al. (2019) <doi:10.1038/s41588-019-0483-y>. Complete model configuration, simulation and analysis pipelines can be therefore constructed without a need to leave the R environment, eliminating friction between disparate tools for population genetic simulations and data analysis.

Maintained by Martin Petr. Last updated 12 days ago.

popgenpopulation-geneticssimulationsspatial-statistics

3.4 match 56 stars 9.15 score 88 scripts