All software below is free of use but please cite it if you use it.

1. R packages and functions

Note: The packages denoted by * are not on CRAN but on github, to install these R packages use the code below.

• The goric function in the R package restriktor on CRAN (in collaboration with Leonard Vanbrabant), which enables using both GORIC and GORICA to evaluate theory-based hypotheses. More details w.r.t. the function can be found via ?goric. Here (on my GitHub page), you can find html tutorial files with R code for the GORIC and GORICA (when having one or more studies). 
• the function evSyn in the restriktor R package and the github package GoricEvSyn*; the latter contains mutiple functions. This function / These functions aggregate/synthesize evidence from multiple (primary) studies. These studies can differ in design (like conpcetioal replications), but should share the same central theory (investigated by study-specific hypotheses). For more details on one of the functions, see ?evSyn and ?GoricEvSyn, resp.. On my GitHub tutorial page, you can also find a vignette/tutorial for aggregating evidence ("Tutorial_GORIC_restriktor_AggrSupport.html"). The evSyn function aggregates evidence using the AIC-type criterion GORICA. The github package GoricEvSyn contains functions that either use the AIC-type criterion GORICA or Bayesian model selection.
• the function calculate_IC_weights in the restriktor R package or the function IC.weights in the github package ICweights*. These functions transforms IC (AIC, GORIC(A), BIC, ...) values into IC weights that quantify the relative strength of hypotheses/models; for more details see ?calculate_IC_weights and ?IC.weights, respectively.
• ICweights*, which contains two functions: IC.weights and ExpectedHypoRate. The first transforms IC (AIC, GORIC(A), BIC, ...) values into IC weights that quantify the relative strength of hypotheses/models; for more details see ?IC.weights. The second calculates, among other things, the expected percentage of times hypotheses are chosen under a specific (null) distribution; for more details see ?ExpectedHypoRate.
• benchmarks*, which contains two functions: benchmarks and benchmarks_ANOVA. These functions calculate benchmarks under a specific (null) population which can be used to compare the obtained (ratio of) GORIC(A) weights with. For more details see ?benchmarks and ?benchmarks_ANOVA, respectively.
• CTmeta*, which enables doing meta-analysis in lagged effects. There are multiple functions, also ones not related to doing CTmeta, which are mentioned in the Shiny app tab of this webpage. See, for instance, ?CTmeta and ?PhiPlot. To see them all use: lsf.str("package:CTmeta") after installing and loading the CTmeta package as mentioned below. On my GitHub page, you can also find a (html and pdf) vignette/tutorial for CTmeta ("Introduction to CTmeta...").
• ChiBarSq.DiffTest* to conduct a (robust Satorra-Bentler) Chi-bar-square test on variances (e.g., testing the fit of a CLPM versus the fit of the RI-CLPM). There is one function, for more details see ?ChiBarSq.DiffTest.
• gorica, in collaboration with Caspar van Lissa and Yasin Altinisik. This R package is on CRAN and enables evaluating theory-based hypotheses with an AIC-type information criterion; which is applicable to a broad range of statistical models.
• goric: Generalized Order-Restricted Information Criterion, in collaboration with Dr. D. Gerhard.

Their citations can be found using the R command: citation("packagename")

On my GitHub page, you can also find different 'repositories' with extra (R) material. For example, in this github repository, you can find vignettes/tutorials, together with data sets used in the examples. To download material from github, you can for instance use:

1. go to Code (green button),

2. download zip (last option in list),

3. unzip it on your machine (that folder is now your working directory).

* These packages are not on CRAN but on github, to install these github R packages use the following code:

library(devtools) # Make sure you have Rtools (and a version which is compatible with your R version).
install_github("rebeccakuiper/packagename") # After this line, the R package is installed and works the same as if you installed it from CRAN.
# In case it does not install, check the error messages to see whether some packages need to be installed first.
library(packagename)
?functionname # To obtain more information about the input including examples.

# To inspect what functions and/or objects are in the package:
help(package = packagename)  # Gives all objects.
lsf.str("package:packagename") # Gives all functions. Note that the package must be attached.
ls("package:packagename") # Gives all objects. Note that the package must be attached.

In case you encounter any difficulties or when you have suggestions for improvement, please e-mail me (R.M.Kuiper@uu.nl).

2. Stand-alone software

Software for ANOVA models (Clarification)

This software can be used to compare group means. When you do not have hypotheses/expectations about the means on forehand, you should use Explanatory ANOVA. In case you do have, that is, you have theory-based hypotheses, then you should use Confirmatory ANOVA or the GORIC, where the GORIC can handle a broader range of hypotheses (i.e., all linear restrictions except for range restrictions).

Software Exploratory and Confirmatory ANOVA

- Comparisons of Means (console)

- Comparisons of Means with Interface (gui)

Related articles: Kuiper & Hoijtink (2010) and Kuiper, Nederhoff, & Klugkist (2015). 

Software Confirmatory ANOVA

- Confirmatory ANOVA (console)

- Confirmatory ANOVA with Interface (gui)

Related article: Kuiper and Klugkist, & Hoijtink (2010). 

Software GORIC (modification AIC which can evaluate several types of order restrictions)

- GORIC_ANOVA (console)

- Additional Information on Simulation Study (pdf)

- GORIC_ANOVA_RHS=r (console)

Related article: Kuiper, Hoijtink, & Silvapulle (2011) with supplement. 

Software for Multivariate Regression Models (Clarification)

This software can be used to evaluate theory-based hypotheses / expectations you have on forehand regarding the regression coefficients in univariate and multivariate regression models.

Software GORIC (modification AIC which can evaluate several types of order restrictions)

- GORIC_Multivariate_Regression (console)

- GORIC_Multivariate_Regression with Interface (gui; beta-version)

- In addition, there is an R package available (with manual)

- GORIC_Multivariate_Regression in Small Samples (console)

Related articles: Kuiper, Hoijtink, & Silvapulle (2012), Kuiper and Hoijtink (2013); R package: Kuiper, Gerhard, & Hothorn (2014) with supplement, and http://cran.r-project.org/web/packages/goric/goric.pdf.

Software for Regression Models with Missing data (Clarification)

This software can be used when you are interested in predictor selection in regression models via AIC(C) and you encountered missing data.

Software AIC and AICC in the Presence of Missing Data for Predictor Selection with One Dependent Variable

- Predictor Selection In Missing Data (console) 

Related article: Kuiper & Hoijtink (2011).

Software for Combining Multiple Studies (Clarification)

This software can be used when you have multiple studies regarding one concept and you want to combine the evidence from all these studies regarding your hypothesis that there is a positive or negative (or no) effect. For example, one might be interested in whether the effect of past on trust (conditional on other variables) is positive, negative or non-existing. Notably, this concept does not need to be modeled the same in all the studies since we combine evidence (not effects).

Software Combining Multiple Studies regarding the effect of one variable of interest. 

- Combining Studies 

Related article: Kuiper, Raub, Buskens, & Hoijtink (2013) with Appendix.