Density Estimation and Random Number Generation with Distribution Element Trees (detpack)
Density estimation for possibly large data sets and conditional/unconditional random number generation with distribution element trees. For more details on distribution element trees see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632>.

Tidy Temporal Data Frames and Tools (tsibble)
Provides a ‘tbl_ts’ class (the ‘tsibble’) to store and manage temporal-context data in a data-centric format, which is built on top of the ‘tibble’. The ‘tsibble’ aims at manipulating and analysing temporal data in a tidy and modern manner, including easily interpolate missing values, aggregate over calendar periods, performing rolling window calculations, and etc.

An Easy SVG Basic Elements Generator (easySVG)
This SVG elements generator can easily generate SVG elements such as rect, line, circle, ellipse, polygon, polyline, text and group. Also, it can combine and output SVG elements into a SVG file.

Automatic Generation of Interactive Visualizations for Popular Statistical Results (autoplotly)
Functionalities to automatically generate interactive visualizations for popular statistical results supported by ‘ggfortify’, such as time series, PCA, clustering and survival analysis, with ‘plotly.js’ <https://plot.ly/> and ‘ggplot2’ style. The generated visualizations can also be easily extended using ‘ggplot2’ and ‘plotly’ syntax while staying interactive.

Post Processing of (Half-)Hourly Eddy-Covariance Measurements (REddyProc)
Standard and extensible Eddy-Covariance data post-processing includes uStar-filtering, gap-filling, and flux-partitioning. The Eddy-Covariance (EC) micrometeorological technique quantifies continuous exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere. It is important for understanding ecosystem dynamics and upscaling exchange fluxes. (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>). This package inputs pre-processed (half-)hourly data and supports further processing. First, a quality-check and filtering is performed based on the relationship between measured flux and friction velocity (uStar) to discard biased data (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>). Second, gaps in the data are filled based on information from environmental conditions (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>). Third, the net flux of carbon dioxide is partitioned into its gross fluxes in and out of the ecosystem by night-time based and day-time based approaches (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).

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