Problem running examples

Dear Normal Matloff,

I have been trying out your R package, however I am having trouble getting the examples provided in the readme file to work.

Code:

Working part:
`> getPE()

pe <- pe[,c(1,2,4,6,7,3)]
head(pe,2)
age sex wkswrkd ms phd wageinc
1 50.30082 0 52 0 0 75000
2 41.10139 1 20 0 0 12300

pfout <- polyFit(pe,2) # quadratic model
getPoly time: 0.021 0.003 0.024 0 0
lm() time: 0.02 0.004 0.027 0 0
newx <- pe[1,] # dummy 1-row data frame
newx <- newx[,-6] # no Y value
newx$age <- 40
newx$sex <- 1
newx
age sex wkswrkd ms phd
1 40 1 52 0 0
`

However, when I run predict() I get the following error:
Called from: predict.polyFit(pfout, newx) Browse[1]> c model.matrix() reported the following error: Error : $ operator is invalid for atomic vectors

R session:
`

sessionInfo()
R version 3.4.2 (2017-09-28)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.1

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] C/UTF-8/C/C/C/C

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] rpart_4.1-13 polyreg_0.4.0 regtools_1.0.0 car_3.0-2
[5] carData_3.0-2 dummies_1.5.6 mvtnorm_1.0-8 FNN_1.1.3
[9] tidyquant_0.5.5 quantmod_0.4-13 TTR_0.23-4 PerformanceAnalytics_1.5.2
[13] xts_0.11-0 zoo_1.8-3 lubridate_1.7.4 gtrendsR_1.4.2
[17] BatchGetSymbols_2.3 rvest_0.3.2 xml2_1.2.0 forcats_0.4.0
[21] stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.0 readr_1.3.1
[25] tidyr_0.8.1 tibble_2.0.1 ggplot2_3.1.0 tidyverse_1.2.1
[29] devtools_1.13.6

loaded via a namespace (and not attached):
[1] nlme_3.1-137 httr_1.3.1 tools_3.4.2 backports_1.1.2 utf8_1.1.4 R6_2.4.0
[7] lazyeval_0.2.1 colorspace_1.3-2 nnet_7.3-12 withr_2.1.2 tidyselect_0.2.5 curl_3.3
[13] compiler_3.4.2 git2r_0.23.0 cli_1.0.1 keras_2.2.4 scales_1.0.0 quadprog_1.5-5
[19] tfruns_1.4 digest_0.6.18 foreign_0.8-71 rio_0.5.16 base64enc_0.1-3 pkgconfig_2.0.2
[25] rlang_0.3.1 readxl_1.3.0 pdist_1.2 rstudioapi_0.9.0 generics_0.0.2 jsonlite_1.6
[31] tensorflow_1.10 zip_1.0.0 magrittr_1.5 Matrix_1.2-14 Rcpp_1.0.0 Quandl_2.9.1
[37] munsell_0.5.0 fansi_0.4.0 abind_1.4-5 reticulate_1.10 partools_1.1.6 stringi_1.2.4
[43] whisker_0.3-2 yaml_2.2.0 kerasformula_1.5.1 plyr_1.8.4 grid_3.4.2 parallel_3.4.2
[49] crayon_1.3.4 lattice_0.20-35 haven_2.0.0 hms_0.4.2 zeallot_0.1.0 pillar_1.3.1
[55] glue_1.3.0 data.table_1.12.0 modelr_0.1.2 cellranger_1.1.0 gtable_0.2.0 assertthat_0.2.0
[61] openxlsx_4.1.0 broom_0.5.0 RSpectra_0.13-1 memoise_1.1.0 deepnet_0.2
`

Any idea whats going on?
Thanks

Start by reproducing the README example with polyFit(pe, 2), the one-row newx data frame, and predict.polyFit(pfout, newx) under the reported R session. Trace the predict.polyFit entry point and model.matrix error; done means prediction succeeds for the shown newx input without the atomic-vector error.