# S. Curtis

#### 7 packages on CRAN

In confirmatory factor analysis (CFA), structural constraints typically ensure that the model is identified up to all possible reflections, i.e., column sign changes of the matrix of loadings. Such reflection invariance is problematic for Bayesian CFA when the reflection modes are not well separated in the posterior distribution. Imposing rotational constraints -- fixing some loadings to be zero or positive in order to pick a factor solution that corresponds to one reflection mode -- may not provide a satisfactory solution for Bayesian CFA. The function 'relabel' uses the relabeling algorithm of Erosheva and Curtis to correct for sign invariance in MCMC draws from CFA models. The MCMC draws should come from Bayesian CFA models that are fit without rotational constraints.

Fits Leroux model in spectral domain to estimate causal spatial effect as detailed in Guan, Y; Page, G.L.; Reich, B.J.; Ventrucci, M.; Yang, S; (2020) <arXiv:2012.11767>. Both the parametric and semi-parametric models are available. The semi-parametric model relies on 'INLA'. The 'INLA' package can be obtained from <https://www.r-inla.org/>.

A procedure that fits derivative curves based on a sequence of quotient differences. In a hierarchical setting the package produces estimates of subject-specific and group-specific derivative curves. In a non-hierarchical setting the package produces a single derivative curve.

Provides functions that fit two modern education-based value-added models. One of these models is the quantile value-added model. This model permits estimating a school's value-added based on specific quantiles of the post-test distribution. Estimating value-added based on quantiles of the post-test distribution provides a more complete picture of an education institution's contribution to learning for students of all abilities. See Page, G.L.; San Mart<c3><ad>n, E.; Orellana, J.; Gonzalez, J. (2017) <doi:10.1111/rssa.12195> for more details. The second model is a temporally dependent value-added model. This model takes into account the temporal dependence that may exist in school performance between two cohorts in one of two ways. The first is by modeling school random effects with a non-stationary AR(1) process. The second is by modeling school effects based on previous cohort's post-test performance. In addition to more efficiently estimating value-added, this model permits making statements about the persistence of a schools effectiveness. The standard value-added model is also an option.

Provides functions that fit hierarchical Gaussian and probit ordinal models. A (covariate dependent) product partition model is used as a prior. If a covariate dependent product partition model is selected, then all the options detailed in Page, G.L.; Quintana, F.A.; (2018) <doi:10.1007/s11222-017-9777-z> are available. If covariate values are missing, then the approach detailed in Page, G.L.; Quintana, F.A.; Mueller, P (2020) <arXiv:1912.13119> is employed. Also included in the package is a function that fits a Gaussian likelihood spatial product partition model that is detailed in Page, G.L.; Quintana, F.A.; (2016) <doi:10.1214/15-BA971>.