Showing 57 of total 57 results (show query)

jumpingrivers

audit.base:Base package for Posit Checks

Base package for sharing classes between posit audit packages. Unlikely to be useful as is.

Maintained by Jumping Rivers. Last updated 24 days ago.

40.1 match 3.38 score 1 scripts 2 dependents

jumpingrivers

audit.connect:Posit Connect Health Check

Posit Connect Health Check. Deploys various content types to assess whether Connect is functioning correctly.

Maintained by Jumping Rivers. Last updated 1 months ago.

39.4 match 2.70 score 1 scripts

jumpingrivers

audit.workbench:RStudio/Workbench User Acceptance Tests

Testing whether data scientists can do what they expect in Posit Workbench.

Maintained by Jumping Rivers. Last updated 23 days ago.

37.5 match 2.00 score

rstudio

shinytest2:Testing for Shiny Applications

Automated unit testing of Shiny applications through a headless 'Chromium' browser.

Maintained by Barret Schloerke. Last updated 1 months ago.

cpp

4.7 match 108 stars 12.08 score 704 scripts 1 dependents

jkcshea

ivmte:Instrumental Variables: Extrapolation by Marginal Treatment Effects

The marginal treatment effect was introduced by Heckman and Vytlacil (2005) <doi:10.1111/j.1468-0262.2005.00594.x> to provide a choice-theoretic interpretation to instrumental variables models that maintain the monotonicity condition of Imbens and Angrist (1994) <doi:10.2307/2951620>. This interpretation can be used to extrapolate from the compliers to estimate treatment effects for other subpopulations. This package provides a flexible set of methods for conducting this extrapolation. It allows for parametric or nonparametric sieve estimation, and allows the user to maintain shape restrictions such as monotonicity. The package operates in the general framework developed by Mogstad, Santos and Torgovitsky (2018) <doi:10.3982/ECTA15463>, and accommodates either point identification or partial identification (bounds). In the partially identified case, bounds are computed using either linear programming or quadratically constrained quadratic programming. Support for four solvers is provided. Gurobi and the Gurobi R API can be obtained from <http://www.gurobi.com/index>. CPLEX can be obtained from <https://www.ibm.com/analytics/cplex-optimizer>. CPLEX R APIs 'Rcplex' and 'cplexAPI' are available from CRAN. MOSEK and the MOSEK R API can be obtained from <https://www.mosek.com/>. The lp_solve library is freely available from <http://lpsolve.sourceforge.net/5.5/>, and is included when installing its API 'lpSolveAPI', which is available from CRAN.

Maintained by Joshua Shea. Last updated 7 months ago.

9.1 match 18 stars 5.33 score 30 scripts

marce10

dynaSpec:Dynamic Spectrogram Visualizations

A set of tools to generate dynamic spectrogram visualizations in video format.

Maintained by Marcelo Araya-Salas. Last updated 17 days ago.

animal-soundsbioacousticsspectrogram

1.7 match 23 stars 5.50 score 34 scripts

framverse

framrsquared:FRAM Database Interface

A convenient tool for interfacing with FRAM access databases in R environments.

Maintained by Ty Garber. Last updated 2 months ago.

1.5 match 6 stars 5.06 score 9 scripts

tverbeke

SDaA:Sampling: Design and Analysis

Functions and Datasets from Lohr, S. (1999), Sampling: Design and Analysis, Duxbury.

Maintained by Tobias Verbeke. Last updated 3 years ago.

3.5 match 2.15 score 14 scripts

epicentre-msf

redcap:R Utilities For REDCap

R utilities for interacting with the REDCap API.

Maintained by Patrick Barks. Last updated 3 months ago.

1.7 match 7 stars 3.45 score 5 scripts

pakabuka

uscoauditlog:United States Copyright Office Product Management Division SR Audit Data Dataset Cleaning Algorithms

Intended to be used by the United States Copyright Office Product Management Division Business Analysts. Include algorithms for the United States Copyright Office Product Management Division SR Audit Data dataset. The algorithm takes in the SR Audit Data excel file and reformat the spreadsheet such that the values and variables fit the format of the online database. Support functions in this package include clean_str(), which cleans instances of variable AUDIT_LOG; clean_data_to_excel(), which cleans and output the reorganized SR Audit Data dataset in excel format; clean_data_to_dataframe(), which cleans and stores the reorganized SR Audit Data data set to a data frame; format_from_excel(), which reads in the outputted excel file from the clean_data_to_excel() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys. format_from_dataframe(), which reads in the outputted data frame from the clean_data_to_dataframe() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys; support_function(), which takes in the dictionary outputted either from the format_from_dataframe() or format_from_excel() function and returns the data as a formatted data frame according to the original U.S. Copyright Office SR Audit Data online database. The main function of this package is clean_format_all(), which takes in an excel file and returns the formatted data into a new excel and text file according to the format from the U.S. Copyright Office SR Audit Data online database.

Maintained by Frederick Liu. Last updated 3 years ago.

3.7 match 1.00 score