|
r-icestaf
|
public |
Functions to support the ICES Transparent Assessment Framework <http://taf.ices.dk> to organize data, methods, and results used in ICES assessments. ICES is an organization facilitating international collaboration in marine science.
|
2025-04-22 |
|
r-icesdatras
|
public |
R interface to access the web services of the ICES (International Council for the Exploration of the Sea) DATRAS trawl survey database <https://datras.ices.dk/WebServices/Webservices.aspx>.
|
2025-04-22 |
|
r-icesadvice
|
public |
A collection of functions that facilitate computational steps related to advice for fisheries management, according to ICES guidelines. These include methods for calculating reference points and model diagnostics.
|
2025-04-22 |
|
r-iceinfer
|
public |
Given two unbiased samples of patient level data on cost and effectiveness for a pair of treatments, make head-to-head treatment comparisons by (i) generating the bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with specified confidence fraction within [0.50, 0.99] that is equivariant with respect to changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge with economic preferences from an ICE map with specified values of lambda, beta and gamma parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves from an ICE map with specified values of lambda, beta and either gamma or eta parameters.
|
2025-04-22 |
|
r-icebox
|
public |
Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.
|
2025-04-22 |
|
r-icdglm
|
public |
Provides an estimator for generalized linear models with incomplete data for discrete covariates. The estimation is based on the EM algorithm by the method of weights by Ibrahim (1990) <DOI:10.2307/2290013>.
|
2025-04-22 |
|
r-icd.data
|
public |
Data from the United States Center for Medicare and Medicaid Services (CMS) is included in this package. There are ICD-9 and ICD-10 diagnostic and procedure codes, and lists of the chapter and sub-chapter headings and the ranges of ICD codes they encompass. There are also two sample datasets. These data are used by the 'icd' package for finding comorbidities.
|
2025-04-22 |
|
r-icc.sample.size
|
public |
Provides functions to calculate the requisite sample size for studies where ICC is the primary outcome. Can also be used for calculation of power. In both cases it allows the user to test the impact of changing input variables by calculating the outcome for several different values of input variables. Based off the work of Zou. Zou, G. Y. (2012). Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Statistics in medicine, 31(29), 3972-3981.
|
2025-04-22 |
|
r-iccbin
|
public |
Assists in generating binary clustered data, estimates of Intracluster Correlation coefficient (ICC) for binary response in 16 different methods, and 5 different types of confidence intervals.
|
2025-04-22 |
|
r-icc
|
public |
Assist in the estimation of the Intraclass Correlation Coefficient (ICC) from variance components of a one-way analysis of variance and also estimate the number of individuals or groups necessary to obtain an ICC estimate with a desired confidence interval width.
|
2025-04-22 |
|
r-icarus
|
public |
Provides user-friendly tools for calibration in survey sampling. The package is production-oriented, and its interface is inspired by the famous popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to 'icarus'. In addition to calibration (with linear, raking and logit methods), 'icarus' features functions for calibration on tight bounds and penalized calibration.
|
2025-04-22 |
|
r-icaff
|
public |
Imperialist Competitive Algorithm (ICA) <http://en.wikipedia.org/wiki/Imperialist_competitive_algorithm> is a computational method that is used to solve optimization problems of different types and it is the mathematical model and the computer simulation of human social evolution. The package provides a minimum value for the cost function and the best value for the optimization variables by Imperialist Competitive Algorithm. Users can easily define their own objective function depending on the problem at hand. This version has been successfully applied to solve optimization problems, for continuous functions.
|
2025-04-22 |
|
r-ica
|
public |
Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).
|
2025-04-22 |
|
r-ic50
|
public |
Calculation of IC50 values, automatic drawing of dose-response curves and validation of compound screens on 96- and 384-well plates.
|
2025-04-22 |
|
r-ic2
|
public |
Lorenz and concentration curves; Atkinson, Generalized entropy and SGini indices (with decomposition)
|
2025-04-22 |
|
r-ic10trainingdata
|
public |
Training datasets for iC10; which implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package.
|
2025-04-22 |
|
r-ibrokers
|
public |
Provides native R access to Interactive Brokers Trader Workstation API.
|
2025-04-22 |
|
r-ibmsunburst
|
public |
Generates Personality Insights sunburst diagrams based on 'IBM Watson' Personality Insights service output.
|
2025-04-22 |
|
r-ibelief
|
public |
Some basic functions to implement belief functions including: transformation between belief functions using the method introduced by Philippe Smets (arXiv:1304.1122 [cs.AI]), evidence combination, evidence discounting, decision-making, and constructing masses. Currently, thirteen combination rules and five decision rules are supported. It can also be used to generate different types of random masses when working on belief combination and conflict management.
|
2025-04-22 |
|
r-ibdlabels
|
public |
Convert "label", "lexicographic", "jacquard" and "vec", full state description vector. All conversions are done to and from "label", as used in IBD_Haplo. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
|
2025-04-22 |
|
r-ibdhaplortools
|
public |
Functions to analyze, plot, and store the output of running IBD_Haplo software package. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
|
2025-04-22 |
|
r-iatscore
|
public |
This minimalist package is designed to quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. Outputted values can be interpreted as effect sizes. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 219, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). The script will tell you how long it took to run the code, the effect size for the participant, and whether that participant should be excluded based on the criteria outlined by Greenwald et al. (2003). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). Three sample datasets are included in this package (labeled 'IAT', 'TooFastIAT', and 'BriefIAT') to practice with.
|
2025-04-22 |
|
r-iatanalytics
|
public |
Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). A sample dataset (titled 'sampledata') is included in this package to practice with.
|
2025-04-22 |
|
r-iasd
|
public |
Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodal or bimodal.
|
2025-04-22 |
|
r-ialsasynthesis
|
public |
Synthesizes information across collaborating research. Created specifically for Integrative Analysis of Longitudinal Studies of Aging (IALSA).
|
2025-04-22 |
