R-universe search: exports:na

Showing 6 of total 6 results (show query)

steffenmoritz

imputeTS:Time Series Missing Value Imputation

Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: 'Mean', 'LOCF', 'Interpolation', 'Moving Average', 'Seasonal Decomposition', 'Kalman Smoothing on Structural Time Series models', 'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) <doi:10.32614/RJ-2017-009>.

Maintained by Steffen Moritz. Last updated 3 years ago.

data-visualization imputation imputation-algorithm imputets missing-data time-series cpp

162 stars 12.18 score 1.9k scripts 27 dependents

nicchr

cheapr:Simple Functions to Save Time and Memory

Fast and memory-efficient (or 'cheap') tools to facilitate efficient programming, saving time and memory. It aims to provide 'cheaper' alternatives to common base R functions, as well as some additional functions.

Maintained by Nick Christofides. Last updated 3 days ago.

cpp openmp

19 stars 7.21 score 73 scripts 2 dependents

msberends

cleaner:Fast and Easy Data Cleaning

Data cleaning functions for classes logical, factor, numeric, character, currency and Date to make data cleaning fast and easy. Relying on very few dependencies, it provides smart guessing, but with user options to override anything if needed.

Maintained by Matthijs S. Berends. Last updated 4 months ago.

32 stars 6.98 score 64 scripts 9 dependents

insightsengineering

autoslider.core:Slide Automation for Tables, Listings and Figures

The normal process of creating clinical study slides is that a statistician manually type in the numbers from outputs and a separate statistician to double check the typed in numbers. This process is time consuming, resource intensive, and error prone. Automatic slide generation is a solution to address these issues. It reduces the amount of work and the required time when creating slides, and reduces the risk of errors from manually typing or copying numbers from the output to slides. It also helps users to avoid unnecessary stress when creating large amounts of slide decks in a short time window.

Maintained by Joe Zhu. Last updated 2 months ago.

4 stars 6.03 score 3 scripts

nelson-gon

manymodelr:Build and Tune Several Models

Frequently one needs a convenient way to build and tune several models in one go.The goal is to provide a number of machine learning convenience functions. It provides the ability to build, tune and obtain predictions of several models in one function. The models are built using functions from 'caret' with easier to read syntax. Kuhn(2014) <doi:10.48550/arXiv.1405.6974>.

Maintained by Nelson Gonzabato. Last updated 10 days ago.

analysis-of-variance anova correlation correlation-coefficient generalized-linear-models gradient-boosting-decision-trees knn-classification linear-models linear-regression machine-learning missing-values models r-programming random-forest-algorithm regression-models

2 stars 5.78 score 50 scripts

manifestoproject

manifestoR:Access and Process Data and Documents of the Manifesto Project

Provides access to coded election programmes from the Manifesto Corpus and to the Manifesto Project's Main Dataset and routines to analyse this data. The Manifesto Project <https://manifesto-project.wzb.eu> collects and analyses election programmes across time and space to measure the political preferences of parties. The Manifesto Corpus contains the collected and annotated election programmes in the Corpus format of the package 'tm' to enable easy use of text processing and text mining functionality. Specific functions for scaling of coded political texts are included.

Maintained by Jirka Lewandowski. Last updated 7 years ago.

55 stars 5.60 score 72 scripts