version 1.3.0

DESCRIPTION

@@ -1,18 +1,19 @@
 Package: lmvar
 Type: Package
 Title: Linear Regression with Non-Constant Variances
-Version: 1.2.1
+Version: 1.3.0
 Author: Posthuma Partners <info@posthuma-partners.nl> 
 Maintainer: Marco Nijmeijer <nijmeijer@posthuma-partners.nl>
 Description: Runs a linear regression in which both the expected value and the variance can vary per observation. The expected values mu follows the standard linear model mu = X_mu * beta_mu. The standard deviation sigma follows the model log(sigma) = X_sigma * beta_sigma. The package comes with two vignettes: 'Intro' gives an introduction, 'Math' gives mathematical details.
 License: GPL-3
 LazyData: TRUE
 Imports: Matrix (>= 1.2-4), matrixcalc (>= 1.0-3), maxLik (>= 1.3-4),
-        stats (>= 3.2.5)
+        stats (>= 3.2.5), parallel (>= 3.4.0), graphics (>= 3.4.0)
 RoxygenNote: 6.0.1
 Suggests: testthat, knitr, rmarkdown, R.rsp, MASS
 VignetteBuilder: knitr, R.rsp
+ByteCompile: true
 NeedsCompilation: no
-Packaged: 2017-06-15 08:16:57 UTC; Marc
+Packaged: 2017-09-07 16:38:03 UTC; Marc
 Repository: CRAN
-Date/Publication: 2017-06-15 15:08:33 UTC
+Date/Publication: 2017-09-07 16:57:40 UTC

MD5

@@ -1,22 +1,24 @@
-2befcdf1a1f14cbb808b975e348afe8c *DESCRIPTION
-4ce3ed1c66a42a72cd5b3febd1f1a16b *NAMESPACE
-dfe1e25621105668314a7b4817e2cec5 *NEWS.md
+8529d3ad0081124a394bb527ee4f4002 *DESCRIPTION
+d7b3b055a54ec3ea20025d0d9ec537f2 *NAMESPACE
+20f3d086eea94d6f505070c1c5bdba97 *NEWS.md
 799ae591e93eb076cea0d9f8150235dd *R/AIC.lmvar.R
 b0137d370c4c1a01bc5ceb87d8cc6285 *R/alias.lmvar.R
 c5a11e9f3431dd97d7e32a2df0f1fbd5 *R/beta_sigma_names.R
 6d527a34b60bbee5b434a3eb19b542a3 *R/coef.lmvar.R
-569f75beeb1ccb83d5a8498860dcb96f *R/cv.lm.R
-2d12baa90e8309faaa22f35879634516 *R/cv.lmvar.R
+718d994f7b143739b6ef42c943db24ec *R/cv.lm.R
+949c597b1db808d95f31a63c802f49c7 *R/cv.lmvar.R
 e2a1a06a401c6301a201079f420b545c *R/dfree.R
 39b90ad72cc9880282de9bf614d68133 *R/examples/AIC_examples.R
 bd9d7d3681defffd188f22a59df22c77 *R/examples/alias_examples.R
 13fa82f04c348bab1ab18187053eea3b *R/examples/beta_sigma_names_examples.R
 c5ae01de2d2f10f7b4bfa919e3f388a6 *R/examples/coef_examples.R
-1e796338615789545a82cea919ed1b26 *R/examples/cv.lm_examples.R
-fb5522048c894ca3ceca0b393601c206 *R/examples/cv.lmvar_examples.R
+e0010ebeb1c03a095f66a011ba8bb36d *R/examples/cv.lm_examples.R
+d7a1f1991629cf6020d032e75506be65 *R/examples/cv.lmvar_examples.R
 9d526fd9bc9b4c72c55cc5cd2614b4fa *R/examples/dfree_examples.R
 dd73ddf4aff15db9a6a6407af1a59577 *R/examples/fisher_examples.R
 de72e46ee5d6f79ef95e6546a123fe54 *R/examples/fitted_examples.R
+7b44ee3e2f417ae8f7b9c360962cba82 *R/examples/fwbw.lm_examples.R
+a39b7511472835d2a3d644b080897516 *R/examples/fwbw.lmvar_examples.R
 3c50a595183a1e9cc4caec85108ce44d *R/examples/lmvar_examples.R
 e93509e8f18367eae4c7a21672482354 *R/examples/logLik_examples.R
 02000462392443088a7b9628b773cd8a *R/examples/nobs_examples.R
@@ -25,52 +27,63 @@ e93509e8f18367eae4c7a21672482354 *R/examples/logLik_examples.R
 ef613a5445aeaf5860f2c5fe24ea69b8 *R/examples/summary_examples.R
 2139927b488f26929811b608c885885f *R/examples/vcov_examples.R
 65c5d7d3efb959034969ce25081d0525 *R/fisher.R
-f7f5e1b02453dee36675c0b93c29e044 *R/fitted.lmvar.R
+55710a0dc6e13d24c95ebd093a0cc634 *R/fitted.lmvar.R
+76d53c81c09306d3c6f9d9ab2720861c *R/fwbw.R
+056de7c2348c29e24f9427c01f92d5a6 *R/fwbw.lm.R
+b6aba946b99d5ab4fb8509b78e8c6093 *R/fwbw.lmvar.R
 d97d4f2dd4613ba9af5c79e8c683d4b7 *R/gaussian_var.R
-9eb4c013478d16c190fc24dbd347d8d1 *R/lmvar.R
+2ad52fdbc43ab3c4307a0c7451301116 *R/lmvar.R
 8a5f8803d15359cd28f236e6a59d3d1d *R/logLik.lmvar.R
-d3f6d391dc6c4b1d11d42cc305cc14c7 *R/make_matrix_full_rank.R
+e5cf148c174d648f080d175de6193a60 *R/make_matrix_full_rank.R
+cd8a438e6f4d4458598f14f385ca60a1 *R/make_matrix_full_rank.default.R
+b1ae7360f0cdf66e3eb8322a0e682d51 *R/make_matrix_full_rank.qr.R
+ce35ea079b9ea28fca1c413ba974790e *R/make_matrix_full_rank.sparseqr.R
 f3288867bdf9afe76559cb414d136f40 *R/matrix_column_names.R
 4f63ac17353588bd3d9497d181ace9cb *R/nobs.lmvar.R
 5668d333949fd9a3e2cf8099b4f1e603 *R/order_10.R
-e34da130a7e9f2e880f57e0985175699 *R/predict.lmvar.R
-6184e108781a1455bf9788ea6a351b36 *R/print.cvlmvar.R
+576c8f6c982e185744f840ebf9594d5e *R/predict.lmvar.R
+e9b6a2bd7ebec9dfe9f6221112b8db99 *R/print.cvlmvar.R
 7cd1d6d1d15de59cc06d82fe4e2ffae0 *R/print.summary_lmvar.R
 68ba53a968caa6fc52a96117a4b2a537 *R/residuals.lmvar.R
-0e3a1edb6a66a8bc3a8674c358204bcf *R/round_to_sd_accuracy.R
+a57fc156653d7c9ed6179fc3068774d1 *R/round_to_sd_accuracy.R
 da4359eb750fb52cb6f5913f4d34f8f3 *R/summary.lmvar.R
 7edbf99718bd27c73eef02ba0b3962bf *R/vcov.lmvar.R
 e4c8237c288027acdfbbff7b085b7e2f *README.md
-8147b6d474cbae50e6870e445787ccde *build/vignette.rds
+8c53f40ce779bcb91efd666d99c94436 *build/vignette.rds
 f82e1918a45923e06c52f5e55835e713 *inst/doc/Intro.R
-2439946a1cce9cce99a20fe0c0a740d1 *inst/doc/Intro.Rmd
-4ff1aab4c952dc60441900fb5ffab43d *inst/doc/Intro.html
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+ae5249a0a4dc5d00c470d00ddaf27121 *inst/doc/Intro.html
 30cacce24029165a47ad57078be7c082 *inst/doc/Math.ltx
-55070256ee4c711e3a7a9dd9fac14479 *inst/doc/Math.pdf
+2aaff8ee1e29c7dfd486da3ab9a9b65f *inst/doc/Math.pdf
 cdc8775371ef46701cbbfdd098441d22 *man/AIC.lmvar.Rd
 40be9e23383ce6cfb87f6158572eab2e *man/alias.lmvar.Rd
 96870ffeb6e33b5476b9014730e99d0f *man/beta_sigma_names.Rd
 080d71de1dad4ff42e68810ec5e3c56f *man/coef.lmvar.Rd
-060f04364c7add68736e43654487c719 *man/cv.lm.Rd
-c9cc42de5fdf7162570c497f6a9fc91e *man/cv.lmvar.Rd
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+194343163cc18b220ac7779ebb479cf8 *man/cv.lmvar.Rd
 d728f54df747c77aa92e6dc2c20e1897 *man/dfree.Rd
 a5e5ccd1c0fafcdaee3bbeb7cab47cd5 *man/fisher.Rd
-0ae10d96bcc680bbf04b1011fe64662e *man/fitted.lmvar.Rd
-782d9d4bfb97c6bd852b8011beb26bfe *man/lmvar.Rd
+e7644d21028376eabaaa0d6be23001d3 *man/fitted.lmvar.Rd
+48b61963e666087c40453b34bcd2f59e *man/fwbw.Rd
+2767d53d216092f2fcc458bd6d698bf2 *man/fwbw.lm.Rd
+618a816bc8e66bdaa03bbdca3c91e485 *man/fwbw.lmvar.Rd
+ff64c128f2e97962b98c5bcac72bee22 *man/lmvar.Rd
 d3041936af6e96cdc0ae4464e0f52796 *man/logLik.lmvar.Rd
 c33fe1105392aeab24e540c8f09459a4 *man/nobs.lmvar.Rd
-4b281bacff81979faa17b3c4aa020d8f *man/predict.lmvar.Rd
+f7d8f046405077977aeeff7a3e9ab0e5 *man/predict.lmvar.Rd
 ae4ae2b043239120febca0d18f449c99 *man/print.cvlmvar.Rd
 080d695ec1395b492167dbf7e898c71a *man/print.summary_lmvar.Rd
 9b35a0b154bc37c64d22c063fe84850e *man/residuals.lmvar.Rd
 7fca9ba8b3a74e8447ce5e544f35fb7e *man/summary.lmvar.Rd
 869b39bcf7108f031d972e1883df6a22 *man/vcov.lmvar.Rd
-2240bdeb95bf161a84eb0780c4f55a4f *tests/create_test_data.R
-b5e9bbfbc1b319bc3baec90d163e1426 *tests/testthat.R
+cbc022948d6c0009e705b9f0111937cf *tests/testthat.R
+2240bdeb95bf161a84eb0780c4f55a4f *tests/testthat/helper.R
+6a3c1e1754a425df52885f76e6efe33b *tests/testthat/test_cross_validations.R
 9181c1bb3ff9b24d1d5da2dcf1a0ce1e *tests/testthat/test_extractors.R
-4f4a499617bdd356720378ffbbfbda7b *tests/testthat/test_gradient_and_hessian.R
-9b102c052b9c2a6634f2ad0f4e23c450 *tests/testthat/test_lmvar.R
+f9c3bdb716810b6674fd79b18a44c2e4 *tests/testthat/test_fwbw.R
+662b803897ac92828a7d5368f62f7e45 *tests/testthat/test_gradient_and_hessian.R
+1fc85f6a930d7cb215c91afc563c4b8d *tests/testthat/test_lmvar.R
 2de1a3ebd02098ee93f67c401f92d281 *tests/testthat/test_predict.R
-2439946a1cce9cce99a20fe0c0a740d1 *vignettes/Intro.Rmd
+3f28d9c0733d19e3ce0a6396832665e2 *vignettes/Intro.Rmd
 30cacce24029165a47ad57078be7c082 *vignettes/Math.ltx
 3bdf41fcf4a9097ce1e06f5367c48ea9 *vignettes/bibliography.bib

NAMESPACE

@@ -4,6 +4,8 @@ S3method(AIC,lmvar)
 S3method(alias,lmvar)
 S3method(coef,lmvar)
 S3method(fitted,lmvar)
+S3method(fwbw,lm)
+S3method(fwbw,lmvar)
 S3method(logLik,lmvar)
 S3method(nobs,lmvar)
 S3method(predict,lmvar)
@@ -17,6 +19,7 @@ export(cv.lm)
 export(cv.lmvar)
 export(dfree)
 export(fisher)
+export(fwbw)
 export(lmvar)
 importFrom(stats,alias)
 importFrom(stats,nobs)

NEWS.md

@@ -1,3 +1,19 @@
+Version 1.3.0
+-------------
+
+* New functions 'fwbw.lm' and 'fwbw.lmvar' for model-selection by means of a forward- / backward-step algorithm. 
+
+* A user-specified function can be cross-validated in 'cv.lm' and 'cv.lmvar'.
+
+* The function 'lmvar' allows one to solve the model under the contraint of minimum standard deviations for all
+observations.
+
+* The k fits in a k-fold cross-validation are executed in parallel to boost performance in case of large models (in 'cv.lm' and 'cv.lmvar').
+
+* Performance improvements for large sparse model matrices of class 'dgCMatrix'
+
+* Fix bug in 'cv.lm' and 'cv.lmvar' in case model matrices have one column only.
+
 Version 1.2.1
 -------------