paulponcet

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lplyr

'dplyr' verbs for lists and other verbs for data frames

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modeest

Mode estimation

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tribe

Play with the tribe of attributes in R

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flifo

Don't get stuck with stacks in R

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strip

Lighten your R model outputs

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bReeze

An R package collecting functions for wind resource assessment

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oak

Trees Creation and Manipulation

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openwindfarm

API Client for ENGIE's Open Wind Farm

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bazar

Miscellaneous basic R functions

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observer

Observe and check your data in R

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Rexec

Make .Rexec files into executable R files under Windows

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statip

Miscellaneous statistical functions in R

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akima

:exclamation: This is a read-only mirror of the CRAN R package repository. akima — Interpolation of Irregularly and Regularly Spaced Data

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dbscan

Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms - R package

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funk

Funky functional programming with R

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httr

httr: a friendly http package for R

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lvplot

Letter value boxplots for R

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magrittr

R package to bring forward-piping features ala F#'s |> operator. Ceci n'est pas un pipe.

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metsyn

Meteo France Synop Dataset

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modelr

Helper functions for modelling

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multidplyr

Partitioned data frames for 'dplyr'

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padr

Padding of missing records in time series

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pipelearner

Machine learning pipelines

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pvlib-python

A set of documented functions for simulating the performance of photovoltaic energy systems.

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pyGAM

[HELP REQUESTED] Generalized Additive Models in Python

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r4ds

R for data science

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shinytemplate

RStudio Project Templates for Shinyapps

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simputation

Making imputation easy

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tidyr

Easily tidy data with spread and gather functions.

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VIM

This package introduces new tools for the visualization of missing and/or imputed values, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missings and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods.

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