Found 113 packages in 0.01 seconds
The Ramer-Douglas-Peucker Algorithm
Pretty fast implementation of the Ramer-Douglas-Peucker algorithm for reducing the number of points on a 2D curve.
Urs Ramer (1972), "An iterative procedure for the polygonal approximation of plane curves"
Estimating Causal Dose Response Functions
Functions and data to estimate causal dose response functions given continuous, ordinal, or binary treatments. A description of the methods is given in Galagate (2016) < https://drum.lib.umd.edu/handle/1903/18170>.
Supplementary Item Response Theory Models
Supplementary functions for item response models aiming
to complement existing R packages. The functionality includes among others
multidimensional compensatory and noncompensatory IRT models
(Reckase, 2009,
Access the Speechs and Speaker's Informations of House of Representatives of Brazil
Scrap speech text and speaker informations of speeches of House of Representatives of Brazil, and transform in a cleaned tibble.
Approximate Conditional Inference for Logistic and Loglinear Models
Implements higher order likelihood-based inference for logistic and loglinear models.
Downloading, Reading and Analyzing PNADC Microdata
Provides tools for downloading, reading and analyzing the Continuous National Household Sample Survey - PNADC, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website < https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.
Statistical Methods for Psychologists
Implements confidence interval and sample size methods that are especially useful in psychological research. The methods can be applied in 1-group, 2-group, paired-samples, and multiple-group designs and to a variety of parameters including means, medians, proportions, slopes, standardized mean differences, standardized linear contrasts of means, plus several measures of correlation and association. Confidence interval and sample size functions are given for single parameters as well as differences, ratios, and linear contrasts of parameters. The sample size functions can be used to approximate the sample size needed to estimate a parameter or function of parameters with desired confidence interval precision or to perform a variety of hypothesis tests (directional two-sided, equivalence, superiority, noninferiority) with desired power. For details see: Statistical Methods for Psychologists, Volumes 1 – 4, < https://dgbonett.sites.ucsc.edu/>.
Varying Coefficient Meta-Analysis
Implements functions for varying coefficient meta-analysis methods. These methods do not assume effect size homogeneity. Subgroup effect size comparisons, general linear effect size contrasts, and linear models of effect sizes based on varying coefficient methods can be used to describe effect size heterogeneity. Varying coefficient meta-analysis methods do not require the unrealistic assumptions of the traditional fixed-effect and random-effects meta-analysis methods. For details see: Statistical Methods for Psychologists, Volume 5, < https://dgbonett.sites.ucsc.edu/>.
Bayesian Linear Mixed-Effects Models
Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting, implementing the methods of Chung, et al. (2013)
Consistent Anonymisation Across Datasets
A simple function that anonymises a list of variables in a consistent way: anonymised factors are not recycled and the same original levels receive the same anonymised factor even if located in different datasets.