Showing 10 of total 10 results (show query)
tidyverse
haven:Import and Export 'SPSS', 'Stata' and 'SAS' Files
Import foreign statistical formats into R via the embedded 'ReadStat' C library, <https://github.com/WizardMac/ReadStat>.
Maintained by Hadley Wickham. Last updated 6 months ago.
427 stars 18.63 score 18k scripts 682 dependents
gesistsa
rio:A Swiss-Army Knife for Data I/O
Streamlined data import and export by making assumptions that the user is probably willing to make: 'import()' and 'export()' determine the data format from the file extension, reasonable defaults are used for data import and export, web-based import is natively supported (including from SSL/HTTPS), compressed files can be read directly, and fast import packages are used where appropriate. An additional convenience function, 'convert()', provides a simple method for converting between file types.
Maintained by Chung-hong Chan. Last updated 3 months ago.
csvcsvydatadata-scienceexcelioriosasspssstata
610 stars 17.10 score 7.8k scripts 74 dependents
larmarange
labelled:Manipulating Labelled Data
Work with labelled data imported from 'SPSS' or 'Stata' with 'haven' or 'foreign'. This package provides useful functions to deal with "haven_labelled" and "haven_labelled_spss" classes introduced by 'haven' package.
Maintained by Joseph Larmarange. Last updated 1 months ago.
havenlabelsmetadatasasspssstata
76 stars 15.04 score 2.4k scripts 98 dependents
sjewo
readstata13:Import 'Stata' Data Files
Function to read and write the 'Stata' file format.
Maintained by Sebastian Jeworutzki. Last updated 2 years ago.
41 stars 10.74 score 1.7k scripts 45 dependents
ddotta
parquetize:Convert Files to Parquet Format
Collection of functions to get files in parquet format. Parquet is a columnar storage file format <https://parquet.apache.org/>. The files to convert can be of several formats ("csv", "RData", "rds", "RSQLite", "json", "ndjson", "SAS", "SPSS"...).
Maintained by Damien Dotta. Last updated 5 months ago.
conversionconvertconvertercsvparquetsasspsssqlitestata
71 stars 7.36 score 27 scripts 1 dependents
anniejw6
modmarg:Calculating Marginal Effects and Levels with Errors
Calculate predicted levels and marginal effects, using the delta method to calculate standard errors. This is an R-based version of the 'margins' command from Stata.
Maintained by Annie Wang. Last updated 4 years ago.
deltamarginmarginal-effectsstata
17 stars 5.89 score 23 scripts
andyphilips
dynamac:Dynamic Simulation and Testing for Single-Equation ARDL Models
While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).
Maintained by Soren Jordan. Last updated 4 years ago.
ardlstatatime-seriestime-series-analysis
7 stars 5.59 score 37 scripts 1 dependents
gabrielerovigatti
prodest:Production Function Estimation
TFP estimation with the control function approach.
Maintained by Gabriele Rovigatti. Last updated 5 years ago.
estimationproductivitystatatfp
37 stars 4.87 score 20 scripts
magosil86
getmstatistic:Quantifying Systematic Heterogeneity in Meta-Analysis
Quantifying systematic heterogeneity in meta-analysis using R. The M statistic aggregates heterogeneity information across multiple variants to, identify systematic heterogeneity patterns and their direction of effect in meta-analysis. It's primary use is to identify outlier studies, which either show "null" effects or consistently show stronger or weaker genetic effects than average across, the panel of variants examined in a GWAS meta-analysis. In contrast to conventional heterogeneity metrics (Q-statistic, I-squared and tau-squared) which measure random heterogeneity at individual variants, M measures systematic (non-random) heterogeneity across multiple independently associated variants. Systematic heterogeneity can arise in a meta-analysis due to differences in the study characteristics of participating studies. Some of the differences may include: ancestry, allele frequencies, phenotype definition, age-of-disease onset, family-history, gender, linkage disequilibrium and quality control thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical statistical theory, documentation and examples.
Maintained by Lerato E Magosi. Last updated 4 years ago.
getmstatisticgwasheartgenes214heterogeneitymeta-analysismstatisticoutlier-studiesstatasystematic-heterogeneity
3 stars 4.41 score 17 scripts
leeper
webuse:Import Stata 'webuse' Datasets
A Stata-style `webuse()` function for importing named datasets from Stata's online collection.
Maintained by Jodi Beggs. Last updated 6 years ago.
data-importdatasetstatastata-datasets
6 stars 3.78 score 8 scripts