Showing 200 of total 305 results (show query)

spatstat

spatstat.model:Parametric Statistical Modelling and Inference for the 'spatstat' Family

Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.

Maintained by Adrian Baddeley. Last updated 1 days ago.

analysis-of-variancecluster-processconfidence-intervalscox-processdeterminantal-point-processesgibbs-processinfluenceleveragemodel-diagnosticsneyman-scottparameter-estimationpoisson-processspatial-analysisspatial-modellingspatial-point-processesstatistical-inference

10.5 match 5 stars 8.89 score 6 scripts 47 dependents

yihui

xfun:Supporting Functions for Packages Maintained by 'Yihui Xie'

Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.

Maintained by Yihui Xie. Last updated 7 days ago.

3.7 match 142 stars 17.93 score 840 scripts 4.2k dependents

hadley

pryr:Tools for Computing on the Language

Useful tools to pry back the covers of R and understand the language at a deeper level.

Maintained by Hadley Wickham. Last updated 1 years ago.

cpp

4.1 match 202 stars 11.80 score 1.9k scripts 56 dependents

tidyverse

purrr:Functional Programming Tools

A complete and consistent functional programming toolkit for R.

Maintained by Hadley Wickham. Last updated 5 months ago.

functional-programming

2.0 match 1.3k stars 21.81 score 59k scripts 6.7k dependents

colearendt

tidyjson:Tidy Complex 'JSON'

Turn complex 'JSON' data into tidy data frames.

Maintained by Cole Arendt. Last updated 2 years ago.

3.4 match 192 stars 10.58 score 522 scripts 7 dependents

topepo

caret:Classification and Regression Training

Misc functions for training and plotting classification and regression models.

Maintained by Max Kuhn. Last updated 1 months ago.

1.7 match 1.6k stars 19.18 score 61k scripts 289 dependents

mlr-org

mlr3extralearners:Extra Learners For mlr3

Extra learners for use in mlr3.

Maintained by Sebastian Fischer. Last updated 3 months ago.

3.5 match 94 stars 9.23 score 474 scripts

hadley

reshape2:Flexibly Reshape Data: A Reboot of the Reshape Package

Flexibly restructure and aggregate data using just two functions: melt and 'dcast' (or 'acast').

Maintained by Hadley Wickham. Last updated 4 years ago.

cpp

1.8 match 210 stars 17.10 score 94k scripts 2.0k dependents

datastorm-open

visNetwork:Network Visualization using 'vis.js' Library

Provides an R interface to the 'vis.js' JavaScript charting library. It allows an interactive visualization of networks.

Maintained by Benoit Thieurmel. Last updated 2 years ago.

1.7 match 548 stars 15.00 score 4.2k scripts 195 dependents

henrikbengtsson

R.utils:Various Programming Utilities

Utility functions useful when programming and developing R packages.

Maintained by Henrik Bengtsson. Last updated 1 years ago.

1.7 match 63 stars 13.69 score 5.6k scripts 806 dependents

ayalaallon

prepdat:Preparing Experimental Data for Statistical Analysis

Prepares data for statistical analysis (e.g., analysis of variance ;ANOVA) by enabling the user to easily and quickly merge (using the file_merge() function) raw data files into one merged table and then aggregate the merged table (using the prep() function) into a finalized table while keeping track and summarizing every step of the preparation. The finalized table contains several possibilities for dependent measures of the dependent variable. Most suitable when measuring variables in an interval or ratio scale (e.g., reaction-times) and/or discrete values such as accuracy. Main functions included are file_merge() and prep(). The file_merge() function vertically merges individual data files (in a long format) in which each line is a single observation to one single dataset. The prep() function aggregates the single dataset according to any combination of grouping variables (i.e., between-subjects and within-subjects independent variables, respectively), and returns a data frame with a number of dependent measures for further analysis for each cell according to the combination of provided grouping variables. Dependent measures for each cell include among others means before and after rejecting all values according to a flexible standard deviation criteria, number of rejected values according to the flexible standard deviation criteria, proportions of rejected values according to the flexible standard deviation criteria, number of values before rejection, means after rejecting values according to procedures described in Van Selst & Jolicoeur (1994; suitable when measuring reaction-times), standard deviations, medians, means according to any percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data frame prep() returns can also be exported as a txt file to be used for statistical analysis in other statistical programs.

Maintained by Ayala S. Allon. Last updated 6 years ago.

5.1 match 15 stars 3.92 score 11 scripts

shinytree

shinyTree:jsTree Bindings for Shiny

Exposes bindings to jsTree -- a JavaScript library that supports interactive trees -- to enable a rich, editable trees in Shiny.

Maintained by Michael Bell. Last updated 4 months ago.

1.7 match 143 stars 9.02 score 228 scripts 8 dependents

venelin

PCMBase:Simulation and Likelihood Calculation of Phylogenetic Comparative Models

Phylogenetic comparative methods represent models of continuous trait data associated with the tips of a phylogenetic tree. Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above models can be associated with different branches of the tree. The PCMBase package is limited to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic models. The PCMFit package provides functionality for inference of these models to tree and trait data. The package web-site <https://venelin.github.io/PCMBase/> provides access to the documentation and other resources.

Maintained by Venelin Mitov. Last updated 8 months ago.

1.8 match 6 stars 7.56 score 85 scripts 3 dependents

skranz

codeUtils:Helper functions for parsing and classifying R code. Useful for domain specific languages.

Very preliminary. May completely change

Maintained by Sebastian Kranz. Last updated 5 years ago.

6.0 match 2.16 score 12 scripts 4 dependents

rtsay1

MTS:All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics. (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.

Maintained by Ruey S. Tsay. Last updated 3 years ago.

cpp

2.0 match 6 stars 6.41 score 272 scripts 6 dependents

fravale

cquad:Conditional Maximum Likelihood for Quadratic Exponential Models for Binary Panel Data

Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) <DOI:10.3982/ECTA7531> and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) <DOI:10.1080/07474938.2015.1060039>. The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) <DOI:10.1016/j.jeconom.2012.03.004>. For large time dimensions of the panel, the computation of the proposed models involves a recursive function from Krailo M. D., & Pike M. C. (1984, Journal of the Royal Statistical Society. Series C (Applied Statistics)) and Bartolucci F., Valentini, F. & Pigini C. (2021, Computational Economics <DOI:10.1007/s10614-021-10218-2>.

Maintained by Francesco Bartolucci. Last updated 1 years ago.

5.4 match 2.15 score 14 scripts

coolbutuseless

ggsvg:SVG Glyphs for Ggplot

Use SVG graphics as glyphs when plotting points with ggplot2.

Maintained by Mike Cheng. Last updated 4 months ago.

1.7 match 140 stars 6.48 score 72 scripts

diegociccia

rnames:Recursive Display of Items in Nested Lists

Recursive display of names and paths of all the items nested within sublists of a list object.

Maintained by Diego Ciccia. Last updated 10 months ago.

5.3 match 2.08 score 4 dependents

pik-piam

piamInterfaces:Project specific interfaces to REMIND / MAgPIE

Project specific interfaces to REMIND / MAgPIE.

Maintained by Falk Benke. Last updated 5 days ago.

1.6 match 6.45 score 29 scripts 7 dependents

tiledb-inc

tiledbcloud:TileDB Cloud Platform R Client Package

The TileDB Cloud Platform API Client Package offers access to the TileDB Cloud service.

Maintained by John Kerl. Last updated 7 months ago.

1.8 match 1 stars 5.22 score 92 scripts

jbryer

TriMatch:Propensity Score Matching of Non-Binary Treatments

Propensity score matching for non-binary treatments.

Maintained by Jason Bryer. Last updated 7 years ago.

1.7 match 13 stars 5.27 score 32 scripts 1 dependents

yhat

yhatr:R Binder for the Yhat API

Deploy, maintain, and invoke models via the Yhat REST API.

Maintained by Greg Lamp. Last updated 8 years ago.

1.8 match 16 stars 4.86 score 57 scripts

sgibb

readMzXmlData:Reads Mass Spectrometry Data in mzXML Format

Functions for reading mass spectrometry data in mzXML format.

Maintained by Sebastian Gibb. Last updated 1 years ago.

1.7 match 5 stars 4.83 score 15 scripts 6 dependents

ropensci

workloopR:Analysis of Work Loops and Other Data from Muscle Physiology Experiments

Functions for the import, transformation, and analysis of data from muscle physiology experiments. The work loop technique is used to evaluate the mechanical work and power output of muscle. Josephson (1985) <doi:10.1242/jeb.114.1.493> modernized the technique for application in comparative biomechanics. Although our initial motivation was to provide functions to analyze work loop experiment data, as we developed the package we incorporated the ability to analyze data from experiments that are often complementary to work loops. There are currently three supported experiment types: work loops, simple twitches, and tetanus trials. Data can be imported directly from .ddf files or via an object constructor function. Through either method, data can then be cleaned or transformed via methods typically used in studies of muscle physiology. Data can then be analyzed to determine the timing and magnitude of force development and relaxation (for isometric trials) or the magnitude of work, net power, and instantaneous power among other things (for work loops). Although we do not provide plotting functions, all resultant objects are designed to be friendly to visualization via either base-R plotting or 'tidyverse' functions. This package has been peer-reviewed by rOpenSci (v. 1.1.0).

Maintained by Vikram B. Baliga. Last updated 6 months ago.

ddfmuscle-forcemuscle-physiology-experimentstetanuswork-loopworkloop

1.3 match 3 stars 5.92 score 46 scripts

skranz

gtree:gtree basic functionality to model and solve games

gtree basic functionality to model and solve games

Maintained by Sebastian Kranz. Last updated 4 years ago.

economic-experimentseconomicsgambitgame-theorynash-equilibrium

2.0 match 18 stars 3.79 score 23 scripts 1 dependents

skranz

distRforest:Distribution-based Random Forest

Extension of the rpart package with added loss functions and random forest functionality.

Maintained by Roel Henckaerts. Last updated 5 years ago.

3.7 match 1.78 score 12 scripts