version 0.1.0

DESCRIPTION

@@ -0,0 +1,60 @@
+Package: galamm
+Title: Generalized Additive Latent and Mixed Models
+Version: 0.1.0
+Authors@R: c(
+    person(given = "Øystein",
+           family = "Sørensen",
+           role = c("aut", "cre"),
+           email = "oystein.sorensen@psykologi.uio.no",
+           comment = c(ORCID = "0000-0003-0724-3542")),
+    person(given = "Douglas", family = "Bates", role = "ctb"),       
+    person(given = "Ben", family = "Bolker", role = "ctb"),
+    person(given = "Martin", family = "Maechler", role = "ctb"),
+    person(given = "Allan", family = "Leal", role = "ctb"),
+    person(given = "Fabian", family = "Scheipl", role = "ctb"),
+    person(given = "Steven", family = "Walker", role = "ctb"),
+    person(given = "Simon", family = "Wood", role = "ctb")
+           )
+Description: Estimates generalized additive latent and
+    mixed models using maximum marginal likelihood, 
+    as defined in Sorensen et al. (2023) 
+    <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and
+    Skrondal (2004)'s unifying framework for multilevel latent variable 
+    modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse 
+    matrix methods, Laplace approximation, and automatic differentiation. The 
+    framework includes generalized multilevel models with heteroscedastic 
+    residuals, mixed response types, factor loadings, smoothing splines, 
+    crossed random effects, and combinations thereof. Syntax for model 
+    formulation is close to 'lme4' (Bates et al. (2015) 
+    <doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019) 
+    <doi:10.1080/00273171.2018.1516541>).
+License: GPL (>= 3)
+URL: https://github.com/LCBC-UiO/galamm,
+        https://lcbc-uio.github.io/galamm/
+BugReports: https://github.com/LCBC-UiO/galamm/issues
+Encoding: UTF-8
+Imports: lme4, Matrix, memoise, methods, mgcv, nlme, Rcpp, Rdpack,
+        stats
+Depends: R (>= 3.5.0)
+LinkingTo: Rcpp, RcppEigen
+LazyData: true
+RoxygenNote: 7.2.3
+Suggests: covr, gamm4, ggplot2, knitr, PLmixed, rmarkdown, testthat (>=
+        3.0.0)
+Config/testthat/edition: 3
+VignetteBuilder: knitr, rmarkdown
+RdMacros: Rdpack
+NeedsCompilation: yes
+SystemRequirements: C++17
+Packaged: 2023-10-07 13:18:49 UTC; oyste
+Author: Øystein Sørensen [aut, cre] (<https://orcid.org/0000-0003-0724-3542>),
+  Douglas Bates [ctb],
+  Ben Bolker [ctb],
+  Martin Maechler [ctb],
+  Allan Leal [ctb],
+  Fabian Scheipl [ctb],
+  Steven Walker [ctb],
+  Simon Wood [ctb]
+Maintainer: Øystein Sørensen <oystein.sorensen@psykologi.uio.no>
+Repository: CRAN
+Date/Publication: 2023-10-09 13:40:06 UTC

MD5

@@ -0,0 +1,182 @@
+bb2fd033dc95fe86bb7c57c759de3788 *DESCRIPTION
+bf859aef27a21cc57e258f828f8bf3bb *NAMESPACE
+dd0e7730cc947503ac00c0a1ecfc4493 *R/RcppExports.R
+3d2ef896822852129be60e51dd113cee *R/VarCorr.R
+11711bc53b69af8852e24fe9fa3a7cdb *R/anova.galamm.R
+789c11753b3b87a7b7fd7567c602bd5b *R/coef.R
+8289bcb40c97dd7001f7c3628323dc30 *R/confint.R
+02a34e5fe08df8ae44549f1bb14f3736 *R/data.R
+801538d022db037710a2d11101c78c32 *R/define_factor_mappings.R
+5e75eb486681d2e36ef697dbaa0fa867 *R/extract_optim_parameters.R
+7df10069c2bd4bd2db551d6e899e89f7 *R/factor_loadings.R
+bfe9030a0441eff18712bc6e755c191d *R/family.R
+c63d51143d8eb71051e24e3a923a85c3 *R/fitted.galamm.R
+b7f676ace3ab9c5c930ef43fd2ce3d1f *R/fixef.R
+095423799ad869fbf51b2fa0eaefd9a7 *R/galamm-package.R
+386f41fad0aa5558a65d2ea9eaf60de2 *R/galamm.R
+3d94659e18a69c89fdb94862fb5d850a *R/galamm_control.R
+a86bbe021195d4ce89072b09b07e6d4e *R/gamm4-functions.R
+243b88a318f89e023e74fb987cfb1e71 *R/logLik.galamm.R
+cb6db16f295054f8f82c465339ecdff9 *R/mgcv-functions.R
+c352ed088bfb8de2e8b22d1f0b00937a *R/misc.R
+5d813b52feebf25751ce37fc74b6b912 *R/plot.galamm.R
+fb75936a03b9e82ad8e759539a9a1aac *R/plot_smooth.R
+9daf2a90666aadcb7c68e4e1bf856cfd *R/predict.galamm.R
+aad13fe1ba6cf83e7155bc3794b17bc7 *R/ranef.R
+0a4c907bf1746fc0c9214a775979cf55 *R/residuals.galamm.R
+2355a6354c1face0b033e105e6d6bc2b *R/sigma.R
+4e12d4d9afc2ae431dc328bcd0a46c28 *R/smooths.R
+8a532a98f7fd3fec33de98319ee58f28 *R/summary.galamm.R
+6be224b6e4bc93c4da93bedf512367f1 *R/vcov.R
+acfe7e7e864467ad56c9acb687a3e814 *README.md
+9143530e6fd9655a0256c0459eed686d *build/partial.rdb
+96f0c749d8bea45d25d89316b482cfc5 *build/vignette.rds
+4742df45a918cfb08792926b0abba808 *data/cognition.rda
+eb63b9040f19e0388a5d929d067f2df7 *data/diet.rda
+2f31daf7f1b8c88c8626d83820a88474 *data/epilep.rda
+9cc878a82d93118c0112b2c4b5609eb5 *data/hsced.rda
+547c01693d3eddeed811b1dc74a77a2a *data/latent_covariates.rda
+603685e10d8d7e743bc819f27f94a47c *data/latent_covariates_long.rda
+68edda6027a260f4bee2706f00ae2b08 *data/mresp.rda
+ebdd5299ca57cf87d6c5425db0ac820c *data/mresp_hsced.rda
+d5a2c16750cae16802474f2160d10d24 *inst/CITATION
+60ee63e44ea943622c86322aa5211d31 *inst/REFERENCES.bib
+defb09dcb2285a46edb6710f822b0eb1 *inst/doc/galamm.R
+07f492bc38bd57c19a2cea977c73c316 *inst/doc/galamm.Rmd
+2560a9b85cc15a45a67cc89328bd1576 *inst/doc/galamm.html
+55779bdd7e173b3be1ac9f7be9a69338 *inst/doc/glmm_factor.Rmd
+12eb45b3a08c3a3998159ba54c56b37c *inst/doc/glmm_factor.html
+d0488a6403cf2efcc3da90e0966a6be9 *inst/doc/latent_observed_interaction.Rmd
+9ba91244b833f4a7d90442b158c0d429 *inst/doc/latent_observed_interaction.html
+1b304df77dc814acdccbc6895772777b *inst/doc/lmm_factor.Rmd
+ffce7cce5dd35f32608aadfb9826db7a *inst/doc/lmm_factor.html
+343217b0aefa3c8f05d7fcf7d4d501e3 *inst/doc/lmm_heteroscedastic.Rmd
+257f828881f4d196ea07bcda1f9997e8 *inst/doc/lmm_heteroscedastic.html
+901236f9f02dcd8196735346e21ae93e *inst/doc/mixed_response.Rmd
+d4e15bd77382e17801ddaded3dcad2a1 *inst/doc/mixed_response.html
+ee51f68b7ab9c11810c62953807482d8 *inst/doc/optimization.Rmd
+f6c86bab0935fe990164001490a9d4aa *inst/doc/optimization.html
+981f840551cc5c8d0f0ad030987dd870 *inst/doc/semiparametric.Rmd
+a3886ffe7ecc3c3a4ec328dcf9f6a0a8 *inst/doc/semiparametric.html
+b534761c3a30a07ae221e384b7e75eeb *inst/figures/galamm.png
+aceda918f3480d7d0b0768ee19ce0ea4 *inst/include/autodiff/common/binomialcoefficient.hpp
+1bff1b5c8d9c6c8ad41044e3b84ad9d4 *inst/include/autodiff/common/classtraits.hpp
+efd90f066b5f10909e6c4e5303014294 *inst/include/autodiff/common/eigen.hpp
+a14a7dbf5cd7aa78ac50d75bf387e8df *inst/include/autodiff/common/meta.hpp
+ea17c27e5689da44f38a3f471b672fde *inst/include/autodiff/common/numbertraits.hpp
+5fced9597e8fc9ef05752dfda00cc94f *inst/include/autodiff/common/vectortraits.hpp
+e15ecc964764ca8662b25a42777b5532 *inst/include/autodiff/forward/dual.hpp
+c936e85d7a89fc99456619e86fa2505a *inst/include/autodiff/forward/dual/dual.hpp
+d5becd26b8a81ee0c81daeffc2fac0bb *inst/include/autodiff/forward/dual/eigen.hpp
+54c065c7205d9f80d3ef00c9b1f92643 *inst/include/autodiff/forward/real.hpp
+99d30f5a4ea306d1941ebfb5c6e22a91 *inst/include/autodiff/forward/real/eigen.hpp
+f96ca7217d19c2de8daf02ea3e0d9647 *inst/include/autodiff/forward/real/real.hpp
+70e0f52e83154a898af82d4f03a72772 *inst/include/autodiff/forward/utils/derivative.hpp
+471f5e6451687d58696f83b04370d583 *inst/include/autodiff/forward/utils/gradient.hpp
+fa7898962a5c007a1446b0a7a98a0af4 *inst/include/autodiff/forward/utils/taylorseries.hpp
+8741533260d0e8a384cb3451e292c861 *inst/include/autodiff/reverse/var.hpp
+a671086f7afa813107509be92bd30251 *inst/include/autodiff/reverse/var/eigen.hpp
+1a26501d58b508e43ebcbf07374a03fd *inst/include/autodiff/reverse/var/var.hpp
+c47bb5715f8c361e339dd0391fc0226f *inst/testdata/test_multiple_factors.rds
+9b1bcd8c3331f4aa65bd49ba307565c5 *man/VarCorr.Rd
+e3796841fca2712895b91a63296b80ac *man/anova.galamm.Rd
+501113690b4d037de0fb468b539ca027 *man/coef.galamm.Rd
+8d1032d7570a8c0338925090b8da211c *man/cognition.Rd
+9a12c82321fa3446bd8d18702f112ec3 *man/confint.galamm.Rd
+544ac73d2e0b7eb17104f2f875d9db7b *man/deviance.galamm.Rd
+2eccdd50d256876e3dd3eba2a901911a *man/diet.Rd
+1c49f4e84790a0c825f24196e0249650 *man/epilep.Rd
+aa249b3ed7efb4e518efcc376286ecb4 *man/extract_optim_parameters.galamm.Rd
+744dbc435431dcf53e945f7f2c2d04fc *man/factor_loadings.galamm.Rd
+3bc595063c5bd596be1460702ba556ed *man/family.galamm.Rd
+32cb276b6dcaf4f2afe9e06868a42cfa *man/figures/README-pressure-1.png
+41cec592b5690eb691942930d9b9b4b1 *man/figures/README-unnamed-chunk-9-1.png
+121ed44be28adc8c1e9599b9ea550fe2 *man/figures/logo.png
+335defa7ed20e96ab8e7f06e5baa1333 *man/fitted.galamm.Rd
+54757ac7788d7f3cef23373564b975e4 *man/fixef.Rd
+85e3b52662892a26aceb330c23f780c4 *man/galamm-package.Rd
+f085d3e8633274eb44e709cb2f19cb2f *man/galamm.Rd
+8e19f313225c652a0b1d47f23907fb15 *man/galamm_control.Rd
+fd197d0d60f63f2e112e655c3d9d2207 *man/hsced.Rd
+666e6b921180757cc534678792ef9cbd *man/latent_covariates.Rd
+a165584aa8b883e613c16d08e86268a4 *man/latent_covariates_long.Rd
+25964596579557faf2dbf2d2d8f30fe7 *man/llikAIC.Rd
+38b5dfd3bdbadd81f146db361c7ed941 *man/logLik.galamm.Rd
+2b55ea5edae0c83f00b0603c996bf15e *man/mresp.Rd
+ff431ad92d02ada3414bda1f23bb35ed *man/mresp_hsced.Rd
+bf116b00b5ef81956115110e525b1268 *man/nobs.galamm.Rd
+8eb3a69afe8d2b1aa1d43b70f35cbcbe *man/plot.galamm.Rd
+c9d7db8ff46f7ba3e78f53cd13b6cac5 *man/plot_smooth.galamm.Rd
+34e7670dc3c37f95851d8ec30b68ee3c *man/predict.galamm.Rd
+2d0361f72218fbec6fa90c3ccaaf33e8 *man/print.VarCorr.galamm.Rd
+e681541b51b24e601fa3e7d3ccad9a25 *man/print.summary.galamm.Rd
+93e356ee3a96cb7d53192218150a3647 *man/ranef.galamm.Rd
+a8005a6b5bf7abf96da4ab145cfd96c5 *man/residuals.galamm.Rd
+0863ad09eb91314bc1690918bed1270d *man/sigma.galamm.Rd
+ae69918a8008ff3ccf3caf79252ed063 *man/sl.Rd
+0d9770943805b205b2e628388f383059 *man/summary.galamm.Rd
+ab84461a8a7708d1a50fffaa18eb2c42 *man/t2l.Rd
+d674f2c8ec8ebf47a0952615a3c2a91b *man/vcov.galamm.Rd
+4c556d12121399a9fb39be481357be03 *src/Makevars
+4c556d12121399a9fb39be481357be03 *src/Makevars.win
+d69c0f67f0f4e603b0b93db1378f86c2 *src/RcppExports.cpp
+680eed55d03053cd3e3e74a9dfc33f84 *src/compute_galamm.cpp
+24f19b1ad596597d455d21172ca4ccf9 *src/data.h
+15846f665b995a937f2a46639a263cde *src/misc.h
+d6aa0706a28e9b8098d35685788a3e46 *src/model.h
+a84562625dcced9c91348700855ccbb9 *src/parameters.h
+bbab5cd8057c67ab29a25fd8e5a1a73d *src/update_funs.h
+2c7a4ed5bc58696b367e3d50932ad1aa *tests/testthat.R
+fca29642db9df2a0c2351aead5ea12f5 *tests/testthat/Rplots.pdf
+cb6253e090618aa82b27923374f0e7d2 *tests/testthat/_snaps/galamm-glmm.md
+0f1a97045b679e60ea9de146c17a22f2 *tests/testthat/_snaps/galamm-heteroscedastic.md
+26484c4d33106e06a8f35d54694fef4b *tests/testthat/_snaps/galamm-latent-covariates-interaction.md
+144bfb4089e6880012126ead82cda9d1 *tests/testthat/_snaps/galamm-lmm.md
+9ed09852349cf610f56b2f2de20663bc *tests/testthat/_snaps/galamm-mixed-resp.md
+10992f7eb9fea1d2f7765f41052cf6e9 *tests/testthat/_snaps/galamm-semiparametric.md
+04888b3ff11164c00cc65a47afd43eb1 *tests/testthat/_snaps/galamm-setup.md
+9b4ec9e02bbb52b4b2e024fb3010728b *tests/testthat/test-galamm-glmm.R
+787c5904c45908647f0bb63b0b5f111d *tests/testthat/test-galamm-heteroscedastic.R
+18eaf25183863590b2ffa8452010c74b *tests/testthat/test-galamm-latent-covariates-interaction.R
+feefcbf3118b2eca5474a2414a5b5e07 *tests/testthat/test-galamm-lmm.R
+9be7478cf19822a6d5b0f081ae1e044d *tests/testthat/test-galamm-mixed-resp.R
+37ad0ec04ed71720086e3e176603914c *tests/testthat/test-galamm-semiparametric.R
+7a3ec41a02cd2c55525408587f2c2488 *tests/testthat/test-galamm-setup.R
+8a6fb0b09366a11dc47ae91252f7db4e *vignettes/MaximumLikelihood_cache/html/__packages
+0143eb0e2c7c176e208ae70ab356f010 *vignettes/MaximumLikelihood_cache/html/unnamed-chunk-3_204e85e5afc2d28321f3e8f10b2db752.RData
+e30d604779d0ed0e8dd3df8d0f37a98d *vignettes/MaximumLikelihood_cache/html/unnamed-chunk-3_204e85e5afc2d28321f3e8f10b2db752.rdb
+e751cf429071a72ef308107d0772ffb8 *vignettes/MaximumLikelihood_cache/html/unnamed-chunk-3_204e85e5afc2d28321f3e8f10b2db752.rdx
+07f492bc38bd57c19a2cea977c73c316 *vignettes/galamm.Rmd
+55779bdd7e173b3be1ac9f7be9a69338 *vignettes/glmm_factor.Rmd
+6685500a05189c660a6883afd541a4a2 *vignettes/glmm_factor_binomial_diagnostic-1.png
+9fe1067d3beabe8aba4fafb4aa552e65 *vignettes/glmm_factor_poisson_diagnostic-1.png
+de449b282ad862dfaa0568907d5fdd21 *vignettes/glmm_factor_poisson_diagnostic_lme4-1.png
+4fb4705028e05dccf369082ecf3c1e32 *vignettes/latent-observed-smooth-1.png
+d0488a6403cf2efcc3da90e0966a6be9 *vignettes/latent_observed_interaction.Rmd
+1b304df77dc814acdccbc6895772777b *vignettes/lmm_factor.Rmd
+c4fa21a268ac1f7c4d03b7b8cf3e1118 *vignettes/lmm_factor_diagnostic_plot-1.png
+343217b0aefa3c8f05d7fcf7d4d501e3 *vignettes/lmm_heteroscedastic.Rmd
+ce7dc96c4b97b2eab4067e2435f30171 *vignettes/lmm_heteroscedastic_diagnostic-1.png
+901236f9f02dcd8196735346e21ae93e *vignettes/mixed_response.Rmd
+ee51f68b7ab9c11810c62953807482d8 *vignettes/optimization.Rmd
+f421c38267d98c43e3b626c780466fd5 *vignettes/semiparametric-gamm-binomial-1.png
+8a44fe81a8e7e5710c9a815b3ff21008 *vignettes/semiparametric-gamm4-binomial-1.png
+e0539abf5b19827417624215f1e51800 *vignettes/semiparametric-gaussian-by-factor1-1.png
+60b764f80625d5b32d1c498cdb22eac1 *vignettes/semiparametric-gaussian-by-factor2-1.png
+e387de842773e98f5d323a78cf01e444 *vignettes/semiparametric-gaussian-factor-1.png
+27f2420ab492b5b3e2235a3e6e1b0f2d *vignettes/semiparametric-gaussian-gamm-diagnostic-1.png
+6d931153505866426450f63d3c40bebb *vignettes/semiparametric-gaussian-gamm-smooth1-1.png
+5f39bee1f4f8accac3880926165d455f *vignettes/semiparametric-gaussian-gamm-smooth2-1.png
+c7379aff597328e502467e85e076dffb *vignettes/semiparametric-gaussian-gamm-smooth2-2.png
+45493436990aa75ab81a716bf17100d7 *vignettes/semiparametric-gaussian-gamm4-diagnostic-1.png
+a0cfd0d5e4342387d76d98f2e2cd8b62 *vignettes/semiparametric-gaussian-gamm4-smooth-1.png
+77709d47ab4b4cf2c760d2393fca40a2 *vignettes/semiparametric-mixed-by-factor1-1.png
+b93b780e7c43e3efac1f327a4996a348 *vignettes/semiparametric-mixed-by-factor2-1.png
+8d859c82053682a6709727acaefa534f *vignettes/semiparametric-mixedresponse-smooths1-1.png
+9fb122bfe923f454b2eacab282a9f2e6 *vignettes/semiparametric-mixedresponse-smooths2-1.png
+19e427b4e99367e07551b576b9986f8b *vignettes/semiparametric-mixedresponse-smooths3-1.png
+9f5820e148ab510b4d509a050ab5df01 *vignettes/semiparametric-multivariate-gaussian-smooth1-1.png
+bc692094c3c83e67c28463540cbad467 *vignettes/semiparametric-multivariate-gaussian-smooth2-1.png
+f39c4178aca4ded1d82568bbd431f581 *vignettes/semiparametric-multivariate-spaghetti-1.png
+8af43b48feb0c47798876269373a3efd *vignettes/semiparametric-spaghetti-plot-1.png
+981f840551cc5c8d0f0ad030987dd870 *vignettes/semiparametric.Rmd

NAMESPACE

@@ -0,0 +1,56 @@
+# Generated by roxygen2: do not edit by hand
+
+S3method(VarCorr,galamm)
+S3method(anova,galamm)
+S3method(coef,galamm)
+S3method(confint,galamm)
+S3method(deviance,galamm)
+S3method(extract_optim_parameters,galamm)
+S3method(factor_loadings,galamm)
+S3method(family,galamm)
+S3method(fitted,galamm)
+S3method(fixef,galamm)
+S3method(logLik,galamm)
+S3method(nobs,galamm)
+S3method(plot,galamm)
+S3method(plot_smooth,galamm)
+S3method(predict,galamm)
+S3method(print,VarCorr.galamm)
+S3method(print,summary.galamm)
+S3method(ranef,galamm)
+S3method(residuals,galamm)
+S3method(sigma,galamm)
+S3method(summary,galamm)
+S3method(vcov,galamm)
+export(VarCorr)
+export(extract_optim_parameters)
+export(factor_loadings)
+export(fixef)
+export(galamm)
+export(galamm_control)
+export(plot_smooth)
+export(ranef)
+export(s)
+export(sl)
+export(t2)
+export(t2l)
+importFrom(Rcpp,sourceCpp)
+importFrom(Rdpack,reprompt)
+importFrom(mgcv,s)
+importFrom(mgcv,t2)
+importFrom(nlme,VarCorr)
+importFrom(nlme,fixef)
+importFrom(nlme,ranef)
+importFrom(stats,anova)
+importFrom(stats,coef)
+importFrom(stats,deviance)
+importFrom(stats,family)
+importFrom(stats,fitted)
+importFrom(stats,gaussian)
+importFrom(stats,logLik)
+importFrom(stats,nobs)
+importFrom(stats,predict)
+importFrom(stats,residuals)
+importFrom(stats,sigma)
+importFrom(stats,vcov)
+useDynLib(galamm, .registration = TRUE)

R/RcppExports.R

@@ -0,0 +1,7 @@
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+marginal_likelihood <- function(y, trials, X, Zt, Lambdat, beta, theta, theta_mapping, u_init, lambda, lambda_mapping_X, lambda_mapping_Zt, lambda_mapping_Zt_covs, weights, weights_mapping, family, family_mapping, k, maxit_conditional_modes, lossvalue_tol, gradient, hessian, reduced_hessian = FALSE) {
+    .Call(`_galamm_marginal_likelihood`, y, trials, X, Zt, Lambdat, beta, theta, theta_mapping, u_init, lambda, lambda_mapping_X, lambda_mapping_Zt, lambda_mapping_Zt_covs, weights, weights_mapping, family, family_mapping, k, maxit_conditional_modes, lossvalue_tol, gradient, hessian, reduced_hessian)
+}
+